JP2012168069A - Map information processor, navigation device, map information processing method and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a map information processor capable of appropriately display a deformed map showing two or more places.SOLUTION: A map information processor comprises: a deformed map information storage unit 101 for storing deformed map information having a deformed map which is not based on an actual measurement result and object identification information for identifying objects displayed on the deformed map; an object identification information reception unit 102 for receiving two or more pieces of object identification information; a deformed map acquisition unit 103 for acquiring at least one deformed map corresponding to the two or more pieces of object identification information received by the object identification information reception unit 102 or two or more deformed maps corresponding to the two or more pieces of object identification information respectively; and an output unit 110 for outputting the deformed map(s) acquired by the deformed map acquisition unit 103.

Description

  The present invention relates to an apparatus for outputting a deformed map.

  Conventionally, there has been known an online map, a vehicle-mounted or portable navigation system, and the like that displays a map corresponding to a survey result. In addition, a technique for displaying an image of a landscape acquired with respect to a position in a real space corresponding to one point on a map has been known. As a system for displaying such a landscape, for example, street view, location view, Patent Literature 1 and the like have been known (for example, see Patent Literature 1).

JP 2007-226580 A (first page, FIG. 1 etc.)

  On the other hand, deformed maps that do not correspond to survey results have been widely used. For example, deformed maps such as tourist information maps and corporate access maps do not correspond to survey results, but they display the vicinity of the destination in an easy-to-understand manner, so it is very important when the user is close to the destination. It is useful.

  However, conventionally, there has been a problem that a deformed map indicating two or more points cannot be appropriately acquired and displayed. As a result, there has been a problem that it is difficult to use the deformed map as navigation when moving between two or more different points such as the current location and the destination, or the departure location and the destination.

  In addition, deformed maps are often created according to specific uses, but simply applying conventional street view and other technologies, the scenery according to the use of deformed maps for each point on the deformed map. There has been a problem that the image cannot be displayed properly.

  The map information processing apparatus according to the present invention is a deformed map in which deformed map information including a deformed map that is not based on an actual measurement result and object identification information that is information for identifying an object displayed on the deformed map is stored. An information storage unit, an object identification information receiving unit that receives two or more object identification information, and one deformed map corresponding to two or more object identification information received by the object identification information receiving unit, or two or more object identification information 2 is a map information processing apparatus including at least a deformed map acquisition unit that acquires at least two deformed maps corresponding to each other, and an output unit that outputs the deformed map acquired by the deformed map acquisition unit.

  With such a configuration, a deformed map indicating two or more points can be appropriately displayed.

  In the map information processing apparatus of the present invention, in the map information processing apparatus, the deformed map information further includes type information which is information indicating a type of the deformed map, and the object identification information receiving unit further includes: The type information is received, and the output unit is a map information processing apparatus that outputs the deformed map so that the deformed map associated with the type information received by the object identification information receiving unit is ranked higher.

  With such a configuration, the deformed map of the type specified by the user can be ranked and presented at the top.

  In the map information processing apparatus of the present invention, in the map information processing apparatus, a deformed map, a deformed map receiving unit that receives one or more object identification information corresponding to the deformed map, and a deformed map received by the deformed map receiving unit. For a map, an attribute value acquisition unit that acquires one or more pre-specified attribute values, type information using the attribute values, and deformed map information that associates the type information with the deformed map and object identification information. A map information processing apparatus further comprising a classification unit that accumulates in the deformed map information storage unit.

  With this configuration, the deformation map type information can be acquired using the attribute value acquired for the deformation map.

  The map information processing apparatus of the present invention is a determination information storage unit in which determination information obtained by machine learning about a relationship between a deformed map type and an attribute value of the deformed map is stored in the map information processing apparatus. A map information processing apparatus for acquiring the type information of the deformed map using the attribute value acquired by the attribute value acquiring unit for the deformed map and the determination information stored in the determination information storage unit It is.

  With this configuration, it is possible to acquire type information along learning data for a deformed map by using machine learning.

  In the map information processing apparatus according to the present invention, in the map information processing apparatus, the classification unit uses the attribute information acquired by the attribute value acquisition unit for the deformed map as the type information of the deformed map. It is a map information processing apparatus acquired according to a rule that is a condition including the above attribute values.

  With this configuration, it is possible to acquire appropriate type information for the deformed map and clearly determine from the rule how the type is determined.

  In the map information processing apparatus of the present invention, in the map information processing apparatus, a deformed map, a deformed map receiving unit that receives one or more object identification information corresponding to the deformed map, an object on the deformed map, and a road An object classifying unit that classifies a path object, which is an object indicating the object, and a geographic object that is an object other than a road, and detects a peer object from the geographic objects classified by the object classifying unit, and uses the peer object to determine the geographic object. And path objects are grouped according to the positional relationship, the type information of the deformed map according to the grouping situation is acquired, and the deformed map information in which the acquired type information is associated with the deformed map and the object identification information, Default A classification section for storing the map information storage unit, a further map information processing apparatus having a.

  With such a configuration, it is possible to acquire appropriate type information according to the appearance status of the geographic object and the path object on the deformed map.

  The map information processing apparatus according to the present invention includes a determination unit that determines whether or not the distance between the deformed maps is long when the deformed map acquiring unit acquires two or more deformed maps in the map information processing device. The map acquisition unit that acquires an actual measurement map that is a map based on an actual measurement result including a point indicated by at least one or more objects arranged in each of the two or more deformed maps when the determination unit determines that it is far And the output unit is a map information processing apparatus that further outputs the actual measurement map acquired by the map acquisition unit.

  With this configuration, even when the distance between the areas indicated by the two deformed maps is long, the situation between the areas can be supplemented with the actual measurement map.

  In the map information processing apparatus of the present invention, in the map information processing apparatus, the output unit combines and outputs two or more deformed maps when the deformed map acquiring unit acquires two or more deformed maps. Information processing apparatus.

  With this configuration, it is possible to view and handle two deformed maps as one deformed map.

  In the map information processing apparatus of the present invention, in the map information processing apparatus, positions on the deformed map corresponding to two or more object identification information corresponding to two or more objects on the deformed map acquired by the deformed map acquiring unit. Each of the information and the position information in the real space, and using the acquired position information, further includes a scale acquisition unit that acquires the scale of the deformed map, and the output unit is one or more acquired by the deformed map acquisition unit Is a map information processing apparatus that outputs the deformed map in association with the scale.

  With this configuration, the scale of the deformed map can be displayed.

  In the map information processing apparatus of the present invention, in the map information processing apparatus, an image acquired from the web at a point indicated by one or more objects on the deformed map according to the type of deformed map acquired by the deformed map acquiring unit. And a guide image acquisition unit that acquires a guide image that is an image including the image, and the output unit is a map information processing apparatus that further outputs the guide image.

  With this configuration, it is possible to easily obtain an image that guides the area indicated by the deformed map.

  Moreover, the map information processing apparatus of the present invention is the map information processing apparatus, wherein the output unit displays the guide image superimposed on the deformed map.

  With this configuration, the user can easily recognize that it is a guide image for the deformed map.

  In the map information processing apparatus of the present invention, in the map information processing apparatus, a deformed map type is an object map that introduces a geographic object, a route map that introduces a route, or both a geographic object and a route. When the deformed map type is an object map, the guide image acquisition unit acquires an image related to a geographic object on the deformed map, and when the deformed map type is a route map, The map information processing apparatus acquires an image related to a path object and acquires an image related to a geographic object on the deformed map for the closest path object on the deformed map when the type of the deformed map is a mixed map.

  With this configuration, it is possible to acquire an appropriate guide image corresponding to the type of deformed map.

  The map information processing apparatus according to the present invention can appropriately display a deformed map indicating two or more points.

  Further, according to the map information processing apparatus of the present invention, it is possible to display a guidance image corresponding to a deformed map.

Block diagram of a map information processing apparatus in an embodiment of the present invention FIG. 2A is a diagram for explaining an example of an attribute value for a geographical feature that can be acquired by the map information processing apparatus (FIG. 2A), and FIG. 2B is a diagram for explaining an example of an attribute value for an image feature (FIG. 2). (B)) Flow chart showing operation of the map information processing apparatus A flowchart for explaining the operation of the map information processing apparatus A flowchart for explaining the operation of the map information processing apparatus Schematic diagram of the map information processing device The figure which shows an example of the deformation map information management table | surface of the map information processing apparatus The figure which shows an example of the input screen of the map information processing apparatus The figure which shows the example of a display of the map information processing apparatus The figure which shows the example of a display of the map information processing apparatus The figure which shows the figure which shows the example of a display of the map information processing apparatus The figure which shows the deformed map which the said map information processing apparatus acquires (FIG. 12 (a), FIG.12 (b)) The figure which shows the deformation map which the same map information processing device outputs The figure which shows the deformed map which the same map information processing apparatus acquires (FIG. 14 (a), FIG.14 (b)) The figure which shows the actual measurement map which the same map information processing device acquired The figure which shows the example of a display of the map information processing apparatus Block diagram of the map information processing apparatus in Embodiment 2 of the present invention Flow chart showing operation of the map information processing apparatus Flow chart showing operation of the map information processing apparatus Flow chart showing operation of the map information processing apparatus The figure which shows an example of the deformed map given to the map information processing apparatus FIG. 22A shows the objects on the deformed map classified by the map information processing apparatus (FIG. 22A), and FIG. 22B shows the object classification management information for managing the classified objects (FIG. 22B). Schematic diagram showing peer objects on a deformed map by the map information processing device Schematic diagram showing the minimum spanning tree composed of objects on the deformed map by the map information processing device The figure which shows the state which divided | segmented the object on the deformed map into the group by the map information processing apparatus The figure which shows the state which divided | segmented the object on the other deformed map by the map information processing apparatus into the group The figure which shows the state which divided | segmented the object on the other deformed map by the map information processing apparatus into the group The figure which shows the deformed map information which the map information processing apparatus acquired The figure which the same map information processor acquires and shows passing information The figure which shows the example of an output of the guidance image of the map information processing apparatus The schematic diagram which shows the image which comprises the guidance image which the map information processing apparatus outputs The figure which shows an example of the external appearance of the computer system in each embodiment of this invention The figure which shows an example of a structure of the computer system

  Hereinafter, embodiments of the map information processing apparatus and the like will be described with reference to the drawings. In addition, since the component which attached | subjected the same code | symbol in embodiment performs the same operation | movement, description may be abbreviate | omitted again.

(Embodiment 1)
FIG. 1 is a block diagram of a map information processing apparatus 1 in the present embodiment.
The map information processing apparatus 1 includes a deformed map information storage unit 101, an object identification information reception unit 102, a deformed map acquisition unit 103, a deformed map reception unit 104, an attribute value acquisition unit 105, a classification unit 106, a determination information storage unit 1061, a determination Unit 107, map acquisition unit 108, scale acquisition unit 109, and output unit 110.

  The map information processing apparatus 1 is an apparatus that outputs map information. The map information processing device 1 is, for example, a navigation device having functions of route search and route guidance, a portable terminal having a map display function, a so-called personal computer (personal computer), a television receiver, and the like. The navigation device is, for example, a portable navigation device or a car navigation device. Further, it may be a digital camera or the like having a navigation function. The mobile terminal having the map display function is, for example, a so-called smartphone or mobile phone.

  The deformed map information storage unit 101 stores one or more deformed map information. The deformed map information includes a deformed map and object identification information that is information for identifying an object displayed on the deformed map. A map is a reduced representation of various objects and phenomena on the surface of the earth. The map here may be considered to mean, for example, a map image. A deformed map is a map that is not based on actual measurement results. The deformed map may be considered as a map that does not accurately correspond to the actual measurement result. A map that is not based on actual measurement results is a map that does not show the same positional relationship as the real world positional relationship or the same distance relationship as the real world distance relationship. The deformed map is, for example, a map that exaggerates at least a part of an area indicated by the map, emphasizes at least a part, or omits at least a part. The deformed map may be an approximate map, for example. In addition, for example, a map created freely by a user based on knowledge, experience, impressions, or a map created by imagination may be considered as a deformed map. The deformed map is, for example, a tourist information map or a company access map. The data structure of the deformed map does not matter. The deformed map may be raster data (bitmap data) or vector data.

  The map includes, for example, an actual measurement map in addition to the deformed map. The actual measurement map is a map based on the actual measurement. A map based on actual measurement is a map showing the same positional relationship as the real world positional relationship or the same distance relationship as the real world distance relationship. The map based on the actual measurement may be a survey map which is a map actually created by surveying, or may be a map substantially equivalent to the map, for example, a map created from aerial photographs. The data structure of the measured map is not limited. The actual measurement map may be raster data (bitmap data) or vector data.

  An object is information displayed on a map such as a deformed map. The object is, for example, information such as characters and images indicating a store, a landmark, a scenic spot, a building, a place name, a point, an address, and the like. In the case of an image, it may be raster data (bitmap data) or vector data. The object may be embedded in the deformed map, or may be arranged on the deformed map. The object identification information is information for identifying the object, for example, information indicating the name of the object. For example, if the object is character data displayed on a map, the object identification information may be the same as this character data. When the object is arranged or embedded in a deformed map as image data such as a raster (bitmap) image or a character outline image, the object identification information is a character string indicated by the image data. It may be character data indicating the reading of Further, when the object is an image such as an icon, the object identification information may be a name of the object indicated by the icon. Further, the deformed map information may have position information (for example, coordinates) indicating a position where the object exists on the deformed map in association with each object identification information. Here, among objects, an object indicating a road is referred to as a path object, and an object indicating other than a road is referred to as a geographic object. However, as a category into which the object is classified, a different category may be provided in addition to the path object and the geographic object. The path object is an object indicating a road name or the like such as “National highway No. 1” or “Kashiwasuji street”. The geographic object is an object indicating a building name or landmark name such as “Kinkakuji” or “Yamashita Park”, or a place name such as “Kobe City” or “Asakusa”.

  Normally, the deformed map and one or more object identification information corresponding to the deformed map are managed by one record of the deformed map information. However, in the deformed map information, the object identification information may be embedded in combination with the deformed map, for example. Further, when the image of the deformed map is information that can hold a layer, the object may be arranged in one layer of the deformed map.

  The deformed map information may further include type information that is information indicating the type of the deformed map. The type of deformed map is, for example, the use or purpose of use of the deformed map. Examples of the deformed map type include an object map, a route map, and a mixed map. The object map is a map for introducing geographic objects shown on the deformed map. The route map is a map that introduces a route to a destination, or the mixed map is a mixed map that introduces both geographic objects and routes. The type information is information indicating these types. For example, the type information includes “object map”, “route map”, “mixed map”, and the like. The storage here is a concept including temporary storage. The deformed map information storage unit 101 is preferably a non-volatile recording medium, but can also be realized by a volatile recording medium.

  The object identification information receiving unit 102 receives two or more object identification information. The object identification information received by the object identification information receiving unit 102 may be any information that can identify the object. For example, at least one character string indicating a store, a landmark, a scenic spot, a building, a place name, a location, an address, or the like. Part information. When the map information processing apparatus 1 has a GPS (Global Positioning System) (not shown) or the like, one of the two or more pieces of object identification information is information (for example, coordinates) of the current position acquired by the GPS. May be object identification information such as a place name of the current location corresponding to. The process of acquiring object identification information such as a place name from the current position can be realized by, for example, a so-called reverse geocoding technique or a database search that associates place names with latitude and longitude. In such a case, if information on the current position is input, it is possible to receive one piece of object identification information as a result. Therefore, it is also possible to accept coordinates such as the current position. It may be considered that the identification information is received.

  Further, the object identification information receiving unit 102 may further receive type information. Note that the type information received by the object identification information receiving unit 102 may be, for example, a part of the type information included in the deformed map information.

  Object identification information and type information reception include, for example, reception of information input from an input device such as a keyboard, mouse, touch panel, reception of information transmitted via a wired or wireless communication line, an optical disk or a magnetic disk The concept includes reception of information read from a recording medium such as a semiconductor memory.

  The object identification information and type information input means may be anything such as a numeric keypad, keyboard, mouse, or menu screen. The object identification information receiving unit 102 can be realized by a device driver of input means such as a numeric keypad or a keyboard, control software for a menu screen, or the like.

  The deformed map acquisition unit 103 obtains one deformed map corresponding to two or more object identification information received by the object identification information receiving unit 102, or two or more deformed maps corresponding to the two or more object identification information, respectively. Get at least. For example, the deformed map acquisition unit 103 stores in the deformed map information storage unit 101 two or more pieces of object identification information that match the two or more pieces of object identification information received by the object identification information receiving unit 102. Search in information. Then, one deformed map associated in common with the two or more detected object identification information is acquired. In addition, when there is no deformed map that is commonly associated with two or more detected object identification information, a deformed map that is individually associated with each of the two or more object identification information is acquired. The match here may be complete match or partial match. Further, when acquiring the deformed map, the corresponding type information may also be acquired.

  Here, it is considered that the deformation map acquisition unit 103 also acquires link information for the detected deformation map to acquire the deformation map. This is because the detected deformation map can be easily read by using the link information.

  Further, the deformed map acquisition unit 103 further selects a deformed map associated with the type information received by the object identification information receiving unit 102 from the deformed maps acquired using the object identification information as described above. You may make it acquire from the deformed map information storage part 101. FIG.

  The deformed map acquisition unit 103 can usually be realized by an MPU, a memory, or the like. The processing procedure of the deformed map acquisition unit 103 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The deformed map receiving unit 104 receives the deformed map and one or more pieces of object identification information corresponding to the deformed map. The one or more pieces of object identification information here are identification information of objects displayed on the corresponding deformed map. The object identification information may be information prepared in advance in association with the deformed map. Further, for example, the information may be information extracted from an object of an image such as a raster (bitmap) image or a character outline image indicating characters combined on the deformed map by a character recognition technique such as OCR. Moreover, when it is the character data arrange | positioned on a map, what extracted this may be sufficient. The deformed map receiving unit 104 may further receive position information (for example, coordinates) indicating a position on the deformed map where an object indicated by one or more pieces of object identification information is arranged.

  It does not matter how the deformed map received by the deformed map receiving unit 104 is the acquired deformed map. For example, the deformed map received by the deformed map receiving unit 104 may be a deformed map acquired by crawling a so-called WEB with a crawler or the like, or a deformed map generated by a user.

  The deformed map received by the deformed map receiving unit 104 and the one or more object identification information corresponding to the deformed map may be a deformed map including one or more object identification information. The deformed map including one or more pieces of object identification information is, for example, a deformed map in which character strings and images indicating the object identification information are arranged on the map. For example, the deformed map receiving unit 104 receives a deformed map in which an object of a separable character string indicating the object identification information is arranged as a deformed map including the object identifying information, and the deformed map receiving unit 104 The deformed map and the object identification information may be acquired by extracting one or more character strings from the deformed map as the object identification information. Further, for example, the deformed map receiving unit 104 receives a deformed map in which objects of an image such as a raster image indicating a character string or a vector image indicating a character outline are combined as a deformed map including object identification information. The deformed map receiving unit 104 may acquire one or more character string objects corresponding to the character string image on the deformed map as object identification information by using a character recognition technique such as OCR.

  In addition, when acquiring object identification information from a deformed map, the deformed map reception part 104 uses the information (for example, coordinate) which shows the position on the deformed map where the object which the one or more acquired object identification information shows is arrange | positioned. You may acquire as positional information on an object.

  The reception of the deformed map and the object identification information described here is, for example, reading from a storage medium or the like, reception via a communication unit, or the like. The deformed map receiving unit 104 can be realized by a device driver for reading data, a communication unit, or the like. Further, the deformed map receiving unit 104 may include an MPU, a memory, software, and the like for executing processing of character recognition technology such as OCR.

  The attribute value acquisition unit 105 acquires one or more attribute values specified in advance for the deformed map received by the deformed map receiving unit 104. The attribute value may be considered as information indicating the characteristics of the deformed map. The attribute value is a physical quantity obtained from a deformed map, for example.

  The attribute value acquisition unit 105 detects, for example, image characteristics (for example, a specific shape in the image, a distribution of pixels in the image, etc.) on the deformed map by image recognition technology, and uses the detection result to You may acquire the above attribute value. Also, the attribute value acquisition unit 105 may acquire one or more attribute values using, for example, the object identification information received by the deformed map reception unit 104 or information indicating the position of the object indicated by the object identification information. good.

  Hereinafter, an example of one or more attribute values acquired by the attribute value acquisition unit 105 will be described. For example, the attribute value acquisition unit 105 acquires one or more of attribute values described below.

  For example, the attribute values acquired by the attribute value acquisition unit 105 can be broadly classified into, for example, attribute values for geographic features of deformed maps and attribute values for image features.

  FIG. 2A is a diagram for explaining an example of attribute values for geographical features that can be acquired by the attribute value acquisition unit 105.

  A “geographic feature” is a map feature of a deformed map. The “geographic feature” is, for example, a physical quantity obtained from a map as an element explaining regional information. For example, the latitude and longitude of the place name described on the map are elements indicating which area in the real space the map describes, and can be used as attribute values indicating geographical features.

  The attribute value of “geographic feature” is further classified into attribute values indicating four features of “display area”, “object”, “distribution”, and “other”. The “display area” is further classified into “real space coordinates”, “MBR (Minimum Bounding Rectangle) area”, and “scale”. “Objects” are classified into “all objects”, “landmarks”, “paths”, “edges”, “districts”, and “nodes”. “Distribution” is classified into “dispersion in image” and “dispersion in real space”. Others are classified into “direction”, “route guidance information”, and “character information other than route guidance”.

  The “real space coordinates” are the coordinates of the east, west, south, and north ends based on the real space coordinates of the object on the deformed map. For example, as “actual space coordinates”, the attribute value acquisition unit 105 acquires a total of four coordinates of each of the east, west, north, and south for one deformed map. For example, among one or more objects arranged on one deformed map, an object (for example, east longitude) whose longitude value of corresponding coordinates (for example, latitude, longitude) in real space coordinates is the most east side The object having the largest value of) becomes the object on the east side, and the coordinates of this object are acquired as the coordinates of the east end. Note that the objects here may be all objects on the deformed map, or may be objects that exclude all objects that match (or do not match) a predetermined rule from all objects. For example, a character string for guiding a route or a character string such as a store advertisement or a caption on a default map as described later may be excluded from the object here. The same applies to the following. The coordinates of the object on the map may be the coordinates of the center and the center of gravity of the object on the map, or may be the coordinates of the four corners of the object. In addition, when there is an image indicating a position beside an object on the map (for example, an image such as a black circle or a pin stabbed), the position indicated by this image may be the coordinates of the object. The coordinates in the real space coordinates corresponding to the object can be obtained by using a so-called geocoding method or the like. For example, the attribute value acquisition unit 105 detects each of the objects located on the east, west, north, and south ends of the objects on the deformed map, and the detected object identification information received by the deformed map receiving unit 104 is detected. By transmitting the object identification information of the object to a WEB site or the like that provides a geocoding service via the Internet or the like, the value of coordinates in the real space coordinates of the object may be acquired from the WEB site. Further, the attribute value acquisition unit 105 detects objects located at the ends of east, west, south, and north from a database (not shown) that associates object identification information such as place names that the map information processing apparatus 1 has with real space coordinates. The value of the coordinates in the real space coordinates of the object may be acquired by searching for and acquiring the coordinates of the real space coordinates corresponding to the object identification information. For geocoding, refer to the following documents, for example.

  Reference 1: “Geocoding”, [online], [Search February 7, 2011], Internet <URL: http://code.google.com/apis/maps/documentation/javascript/v2 /services.html#Geocoding>

  Here, the area of the MBR is an area of a minimum rectangular area (MBR) including all objects on the default map in the real space. However, instead of all the objects, objects excluding some objects may be used in accordance with a rule specified in advance. The same applies to the following other attribute values. The coordinates indicating the position of each object on the default map in the real space can be acquired in the same manner as described above. The algorithm for acquiring the MBR is a known technique, and thus detailed description thereof is omitted here. Please refer to the following documents for MBR.

  Reference 2: Chiyako Matsumoto, Qiang Ma, and Katsumi Tanaka Web Information Retrieval Based on the Localness Degree Proceedings of the 13th int'l Conf. On Database and Expert System Applications 2002 (DEXA'02), pages 172-181, 2002.

  Reference 3: Naoharu Yamada, Ryong Lee, Hiroki Takakura, and Yahiko Kambayashi Classification of Web Pages with Geographic Scope and Level of Details for Mobile Cache Management The 2nd Int. Workshop on Web Geographical Information Systems, IEEE CS Press, Singapore, Dec. 2002 .

  “Scale” is an attribute value indicating the scale of the deformed map. For example, the attribute value acquisition unit 105 acquires and acquires position information on the deformed map corresponding to two or more object identification information corresponding to two or more objects on the deformed map and position information in the real space, respectively. The scale of the deformed map is acquired using the position information. Specifically, the attribute value acquisition unit 105 uses the object identification information corresponding to two or more objects on the deformed map received by the deformed map receiving unit 104, and the position information of the two or more objects on the deformed map. (For example, coordinates) and position information (for example, coordinates) in real space, and using this position information, the distance on the deformed map between two or more points indicated by these two or more objects, Each distance in the space is acquired, and the acquired distance is used. Specifically, the scale is obtained by dividing the distance on the deformed map by the distance in the real space or displaying the fraction. The distance on the deformed map can be obtained, for example, by acquiring coordinates on the deformed map of two or more objects and calculating the distance between the coordinates. The distance on the deformed map may be expressed in units such as pixels, for example, or may be expressed in units converted into distances such as inches or centimeters using the resolution of the output screen. Similarly to the above, the distance in the real space can be acquired by acquiring the coordinates of the real space at the points indicated by the two or more objects, and calculating the distance between these coordinates. The distance between the coordinates in the real space is expressed in units of distance, for example. Further, the attribute value acquisition unit 105 calculates the MBR area including the object on the deformed map as described above and the MBR area including the position of the object in the real space, and calculates the scale from the area ratio. You may do it.

  “All objects” is an attribute value indicating the number of objects on the deformed map, and is obtained by counting the number of all objects displayed on the deformed map. Instead of counting all objects, the number of object identification information associated with the deformed map may be counted. However, all the objects described here are all objects for the sake of convenience. For example, some objects such as names of prefectures, names of seas, mountains, and rivers may be excluded from the beginning. . The same applies to the following.

  “Path” is an attribute value indicating the number and percentage of path objects displayed on the deformed map. A path is a path through which a human may pass. However, here, as an example, train routes are also assigned to paths. The attribute value acquisition unit 105 searches for a character string that matches the object identification information of each object on the deformed map, for example, in a database (not shown) in which a character string of a path prepared in advance is stored. If there is something to do, this object is determined as a path object. This match may be a complete match or a partial match. This database may be included in the map information processing apparatus 1 or may be included in an external apparatus (not shown) that can be accessed via a network or the like. Alternatively, one or more character strings (for example, roads, national roads, prefectural roads, highways, route lines, lines, railways, etc.) characteristic of the path are prepared in advance on a storage medium (not shown) as a clue phrase. The object identification information including the clue phrase at the beginning or end may be determined as the object identification information of the path. The number of objects in the path is obtained by counting the number of objects determined as a path or the number of object identification information. The ratio is a value obtained by dividing the number of objects in this path by the total number of objects described above. The ratio may be displayed as a percentage as appropriate.

  “Edge” is an attribute value indicating the number and ratio of objects indicating linear elements that are not paths on the deformed map. An element on a line that is not a path is, for example, a river. For example, as in the case of the path, the attribute value acquisition unit 105 uses each database on the deformed map by using a database (not shown) in which character strings of elements on a line that is not a path prepared in advance are stored. It may be determined whether the object is an object of an element on a line that is not a path. This database may be included in the map information processing apparatus 1 or may be included in an external apparatus (not shown) that can be accessed via a network or the like. Alternatively, one or more character strings (for example, river, river, first grade, second grade, etc.) that are characteristic of elements on a line that is not a path are prepared in advance in a storage medium (not shown) and match any of these. The object identification information having the character string to be determined may be determined as identification information indicating an element on a line other than the path. The method of acquiring the number and ratio is the same as in the case of the pass.

  “Disc” is an attribute value indicating the number and ratio of objects indicating a discretion. A discreet is, for example, an area having the same characteristics at various locations in the interior, and names such as municipalities are assigned here. For example, as in the case of the path, the attribute value acquisition unit 105 uses, for example, a database (not shown) in which a character string of a prepared disc is stored, It may be determined whether the object is a discrete object. This database may be included in the map information processing apparatus 1 or may be included in an external apparatus (not shown) that can be accessed via a network or the like. Alternatively, one or more character strings (for example, prefectures, municipalities, etc.) that are characteristic of the discreet are prepared in advance in a storage medium (not shown), and have a character string that matches any of these. The object identification information may be determined as identification information of an object indicating a discrepancy. The method of acquiring the number and ratio is the same as in the case of the pass.

  “Node” is an attribute value indicating the number and ratio of objects indicating a node. A node is an element corresponding to a node or a set point. The node is, for example, a station or a bus stop. For example, as in the case of the path, the attribute value acquisition unit 105 uses, for example, a database (not shown) in which a character string of a node prepared in advance is used to convert each object on the deformed map into a node It may be determined whether or not it is an object. This database may be included in the map information processing apparatus 1 or may be included in an external apparatus (not shown) that can be accessed via a network or the like. Alternatively, one or more character strings (for example, a station, a station, a bus stop, a stop, a bus stop, etc.) characteristic of the node are prepared in advance in a storage medium (not shown), and a character string that matches any of these character strings. May be determined as identification information indicating a node. The method of acquiring the number and ratio is the same as in the case of the pass.

  “Landmark” is an attribute value indicating the number and proportion of landmark objects displayed on the deformed map. A landmark is a building or a monument that represents a local feature or is a symbol. Alternatively, it may be a landmark of the land. Here, the attribute value acquisition unit 105 determines that the object on the default map that is not one of the above-described path, edge, discrete, and node is a landmark object. However, as in the case of the path, the attribute value acquisition unit 105 uses, for example, object identification information of each object on the deformed map in a database (not shown) in which a character string of a landmark prepared in advance is stored. If there is a character string that matches, the object may be determined as a landmark object. In addition, one or more character strings (for example, tower, castle, ruins, monument, etc.) characteristic of landmarks are prepared in advance in a storage medium (not shown), and a character string that matches any of these is selected. The object identification information that is included may be determined as landmark identification information. It may be determined as identification information indicating a node. The method of acquiring the number and ratio is the same as in the case of the pass.

  "Dispersion in image" and "Distribution in real space" are attribute values indicating the dispersion value related to the coordinates of the default map and the dispersion value related to the coordinates in the real space, respectively, of the object displayed on the default map. The attribute value acquisition unit 105 acquires a value obtained by multiplying the variance value in the x-axis direction, the variance value in the y-axis direction, and the biaxial variance. The coordinates in the real space can be obtained by the geocoding technique or the like as described above. Further, since the calculation method of the variance is a known technique, the description thereof is omitted here.

  “Direction” is an attribute value indicating the direction indicated by the deformed map. Here, as an example, the angle between the direction indicated by the positive direction of the y-axis of the deformed map and the north direction is acquired, and this is expressed as “direction”. " For example, the direction is acquired from the positional deviation of the positional relationship between the point indicated by two or more objects in the default map and the point of the real space coordinates indicated by the object. For example, an angle formed by a straight line connecting points indicated by two objects in the default map and a straight line connecting points of real space coordinates indicated by the object is calculated as a direction value.

  “Route guidance information” is an attribute value indicating the presence or absence of route guidance information. Information on the number of route guidance information may be used. The route guidance information is a character string for guiding a route on the default map. The attribute value acquisition unit 105 prepares one or more character strings characteristic of the route guidance information (for example, a destination, a way to go, an access, and a person to come) in advance in a storage medium (not shown) Object identification information including a character string that matches one or more of these one or more character strings is determined as object identification information of an object indicating route guidance information. And the presence or absence of route guidance information and the number are acquired. The “route guidance information” can be acquired from, for example, character string information extracted from the deformed map by OCR or the like. The same applies to the following “character information other than route guidance information”.

  “Character information other than route guidance information” is a character string other than route guidance information, and is an attribute value indicating whether or not there is information on a character string other than an object such as a landmark or a path. Information on the number of character strings other than the route guidance information may be used. The route guidance information is, for example, a character string such as a store advertisement or a caption. The attribute value acquisition unit 105, for example, sets object identification information including a character string that is equal to or more than a predetermined number of character strings and does not match any of the one or more character strings as described above other than route guidance information. It is determined as the object identification information of the object indicating the character information. And the presence or number of character strings other than route guidance information and the number are acquired.

  FIG. 2B is a diagram for explaining an example of attribute values for image features that can be acquired by the attribute value acquisition unit 105.

  The “image feature” is a feature as an image that the deformed map has. An image feature is, for example, a physical quantity obtained from an image as an element that represents information as an image. For example, the lightness in the color component and the lightness in the color component are elements indicating the brightness of the image and can be handled as physical quantities. Since the color and size of the image affect the impression and visibility of the map, it can be estimated that it is related to the user's selection.

  “Image features” are roughly classified into attributes of “shape” and “color”. The “shape” further has an attribute value “image size”. The “color” has attribute values “R”, “G”, “B”, and “V”.

  “Image size” is the number of pixels of the image of the deformed map. For example, the attribute value acquisition unit 105 acquires the vertical, horizontal, and total number of pixels of the deformed map as image size attribute values.

  “R”, “G”, and “B” are values indicating the average and standard deviation of all pixels of the image of the deformed map in each element of the RGB color model, and these values are the values of each pixel. It is calculated by acquiring R value, G value, B value and the like.

  “V” is a value indicating the average and standard deviation of the luminance of all the pixels of the deformed map image, and this value can be calculated from the luminance value of each pixel.

  The attribute values of these image features can be acquired from information on each pixel constituting the image of the deformed map, image properties, header information, and the like. Such processing for acquiring image feature attribute values from an image is a known technique in image processing apparatuses and the like.

  The acquisition of the attribute value here includes reception of the attribute value input from the user, reading of the attribute value stored in the storage medium, reception of the attribute value received via the communication means, etc. It may be considered as a concept that includes For example, the attribute value acquisition unit 105 may receive an attribute value detected by the user visually or the like, or an attribute value acquired for the deformed map by the same process as described above using another device or the like.

  The attribute value acquisition unit 105 can be usually realized by an MPU, a memory, or the like. The processing procedure of the attribute value acquisition unit 105 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The classification unit 106 acquires type information about the deformed map received by the deformed map receiving unit 104 using one or more attribute values acquired by the attribute value acquiring unit 105. Then, the deformed map information in which the acquired type information is associated with the deformed map and the object identification information received by the map receiving unit 104 is accumulated in the deformed map information storage unit 101.

  For example, the classification unit 106 acquires type information about the deformed map received by the deformed map receiving unit 104 by using (1) machine learning and (2) rules. Hereinafter, each case will be sequentially described.

(1) Acquisition by Machine Learning The classification unit 106 uses, for example, two or more attribute values acquired by the attribute value acquisition unit 105 and determination information stored in a determination information storage unit 1061 to be described later. The determination information for acquiring the type information about the deformed map received by the receiving unit 104 is, for example, information obtained as a result of machine learning of the type of deformed map prepared in advance and the attribute value of the deformed map. Therefore, the determination information can be used for determining the type information of the new deformed map.

  Here, a case where SVM (Support Vector Machine) is used as machine learning will be described as an example. Since SVM is a known technique, detailed description thereof is omitted here. For the SVM, refer to the following document, for example.

  Reference 4: Takio Kurita "Introduction to Support Vector Machine", [online], July 18, 2002, [Search January 31, 2011], Internet <URL: http://home.hiroshima-u.ac .jp / tkurita / lecture / svm.pdf>

  As machine learning, machine learning other than SVM, for example, SVR or decision tree may be used.

  For example, first, a plurality of deformed maps acquired from WEB or the like by crawling using a crawler or the like is prepared. Then, for each prepared deformed map, a determination result as to whether or not it is one type of deformed map specified in advance, and a plurality of attribute values (features) among the attribute values that can be acquired by the attribute value acquiring unit 105. ) And a set. The determination result of whether or not the map is one type of deformed map is, for example, a determination result determined by a human looking at the deformed map. A plurality of attribute values may be automatically acquired by the attribute value acquisition unit 105 or a similar processing unit.

  Then, the classification unit 106 that performs machine learning using a set of the determination result of the deformed map type and the plurality of attribute values acquired from the deformed map for a plurality of deformed maps prepared in this manner as teacher data. To give. The classification unit 106 performs machine learning using these data as teacher data. The determination information acquired by the classification unit 106 through machine learning is accumulated in a determination information storage unit 1061 described later.

  Then, the classification unit 106 uses the two or more attribute values acquired by the attribute value acquisition unit 105 for one deformed map and the determination information stored in the determination information storage unit 1061 to use the two or more attribute values. The determination result of whether or not the deformed map corresponding to is a type of deformed map is obtained as a solution. In this case, the classification unit 106 may be considered as a determiner that refers to the determination information and determines whether or not the deformed map corresponding to two or more attribute values is one type. If the map is one type of deformed map, the classification unit 106 acquires type information indicating the one type.

  Note that instead of the machine learning by the classifier 106, a processing unit or device that performs other machine learning is caused to perform machine learning using the same teacher data as described above, and the determination obtained by machine learning. Information may be accumulated in the determination information storage unit 1061.

  In addition, since the machine learning by the above SVM is for determining whether or not the deformed map is one type, for example, whether or not the deformed map is a map of any one of a plurality of types. In order to determine whether or not, teacher data is prepared for each type, machine learning is performed for each type, and the determination information obtained by learning is accumulated in the determination information storage unit 1061 for each type. To keep. Then, by using two or more attribute values acquired from the deformation map to be determined, until the deformation map is determined to be one type, the determination of the type of the deformation map using the type-specific determination information is performed. The determination information may be sequentially changed while being changed. For example, if machine learning is performed in advance for each type of object map, route map, and mixed map as described above, and determination information obtained by machine learning for each type is accumulated in the determination information storage unit 1061. Good.

  It should be noted that two or more types of attribute values used for the teacher data used when performing machine learning and two or more input when obtaining a solution (that is, a determination result) using the determination information obtained by machine learning. The attribute type is the same attribute.

  As described above, when machine learning is performed for each type, the types of two or more attribute values of teacher data used for each type may be different. By appropriately selecting the features to be used as teacher data (here, the type of attribute value), the features unnecessary for prediction are reduced and the accuracy of prediction by machine learning is improved. For each type of deformed map to be determined, It is preferable to use an attribute value of an appropriate attribute.

  In order to obtain an appropriate attribute when evaluating the deformed map type, for example, an attribute having a large influence on the evaluation is extracted from a plurality of attributes prepared in advance, or an attribute having a small influence is selected. Just remove it. For example, a variable selection method, ridge regression, or the like is known as a method for extracting an attribute having a large influence on evaluation from a plurality of attributes. Further, for example, the process of removing an attribute having a small influence on the evaluation from a plurality of attributes can be performed based on a sequential selection method.

  As a result of earnest research by the inventors of the present application, in the machine learning used to determine the object map as described above, the ratio of the path, the number of objects, the coordinates of the north end, the area of the real space, the route guidance information, the size of the image It was found that it is appropriate to use attribute values such as the number of landmarks, the ratio of landmarks, the number of nodes, the number of paths, and the like. Here, the most preferable attribute values are shown in order. By using a pair of two or more of these attribute values acquired from the deformed map and the result of determining whether or not it is an object map for the same deformed map as the teacher data, the deformed map is more appropriately and accurately obtained. It is possible to determine whether or not is an object map.

  Further, as a result of earnest research by the inventors of the present application, in the machine learning used for the determination of the route map as described above, the coordinates of the east end, the number of districts, the variance in the image, the number of objects, the ratio of the path, the west end It was found that it is appropriate to use attribute values such as the coordinates of Y, the Y-coordinate dispersion in the image, the coordinates of the south end, the number of landmarks, and the average of B. Here, the most preferable attribute values are shown in order. By using a pair of two or more of these attribute values acquired from the deformed map and the determination result of whether or not the route map is the same deformed map as the teacher data, the deformed map is more appropriately and accurately used. It is possible to determine whether or not is a route map.

  The classification unit 106 may determine that a deformed map type that is neither an object map nor a route map is a mixed map.

(2) Acquisition by Rule The classification unit 106 acquires, for example, type information of the deformed map according to a rule specified in advance using the attribute value acquired by the attribute value acquiring unit 105 for the deformed map. This predesignated rule is a condition that includes one or more attribute values acquired by the attribute value acquisition unit 105. This rule is, for example, a combination of a condition for one or more attribute value values and type information acquired when the condition is satisfied (or not satisfied). The condition for the value of one or more attribute values is, for example, a condition that one or more attribute values specified in advance are all equal to or greater than a threshold value (or less than a threshold value), or two or more attribute values specified in advance. This is a condition that one or more attribute values are greater than or equal to a threshold value (or less than a threshold value), or a condition obtained by combining a plurality of these conditions. Note that threshold values, rules, and the like may be stored in advance in a storage medium or the like (not shown).

  Further, for example, according to this rule, a value obtained when an attribute value acquired by the attribute value acquisition unit 105 is substituted into a formula prepared in advance using one or more attribute values as variables is determined from a predetermined threshold value. Is a rule that determines that the type information is one type of information.

  For example, when the rule used by the classification unit 106 satisfies the condition that the ratio of the path acquired by the attribute value acquisition unit 105 is equal to or less than the threshold, the object map is acquired as the corresponding deformed map type information. If it is a rule, in this case, if the path ratio is less than or equal to the threshold, the classification unit 106 acquires type information called an object map. In addition, if the rule used by the classification unit 106 is a rule that acquires a route map as type information of a corresponding deformed map when a condition that a path ratio is equal to or greater than a threshold is satisfied, in this case, If the path ratio is equal to or higher than the threshold, the classification unit 106 acquires type information called a route map.

  The classification unit 106 can be usually realized by an MPU, a memory, or the like. The processing procedure of the classification unit 106 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The determination information storage unit 1061 stores determination information. The determination information is information obtained by machine learning about the relationship between the type of deformed map and the attribute value of the deformed map. For example, the determination information is obtained by machine learning using a human-determined type and two or more attribute values acquired from the deformed map for each of a plurality of deformed maps prepared in advance as teacher data. Information. This machine learning may be performed by the determination unit 107 described above, or may be performed by an external device or the like. The storage here is a concept including temporary storage. The determination information storage unit 1061 is preferably a nonvolatile recording medium, but can also be realized by a volatile recording medium.

  The determining unit 107 determines whether the distance between the deformed maps is long when the deformed map acquiring unit 103 acquires two or more deformed maps. The determination unit 107 determines, for example, whether the distance between the deformed maps is long using the positions of two or more objects on two or more maps. The determination as to whether the distances between the deformed maps are close may be based on whether the distance between the regions indicated by the deformed maps is far. The determination unit 107 may determine how the distance between the deformed maps is long. For example, when one or more common objects exist in each of two or more deformed maps, the determination unit 107 determines that the distance between the deformed maps is not far, and otherwise determines that the distance is far. The common object may be an object whose corresponding object identification information matches. Further, when at least a part of real space areas (for example, real space MBRs) indicated by the deformed map overlap, it is determined that the distance is not far (close), and the other cases are determined to be far. Also good. In addition, when the distance between the objects located on the two or more deformed maps that are closest to each other in the corresponding real space is calculated, and this distance is equal to or less than a predetermined threshold value , It may be determined that the distance is not far (near), and other cases may be determined to be far. The information indicating the position of the object in the real space on the deformed map and the information indicating the position and range of the real space indicated by the deformed map are, for example, the attribute value acquisition unit 105 using geocoding or the like. It can be acquired by a process similar to the process of acquiring coordinates and the process of acquiring coordinates such as the vertices of the MBR in real space. Alternatively, the determination unit 107 may receive these pieces of information acquired by the attribute value acquisition unit 105.

  If one or more of the two or more deformed maps acquired by the deformed map acquiring unit 103 includes all of two or more objects indicated by the two or more object identifying information received by the object identifying information receiving unit 102, The determination unit 107 does not have to perform the above processing.

  The determination unit 107 can be usually realized by an MPU, a memory, or the like. The processing procedure of the determination unit 107 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  When the determination unit 107 determines that the map acquisition unit 108 is far, the map acquisition unit 108 is an actual measurement map that is a map based on an actual measurement result including points indicated by at least one or more objects arranged in each of the two or more deformed maps. To get. The at least one object arranged in each of the two or more deformed maps means one or more objects on each deformed map. It does not matter how one or more objects in each deformed map are selected. For example, the map acquisition unit 108 acquires information on the position in the real space for each object on each deformed map, calculates the distance in the real space between the objects selected from each of the deformed maps, and this distance is The closest combination of objects is selected, and an actual measurement map including these two objects is acquired. For example, the map information processing apparatus 1 has a database or the like (not shown) of an actual map in advance, and the actual map may be acquired from this database. Further, the map acquisition unit 108 may acquire an actual measurement map from WEB or an external device via a communication means (not shown). In addition, since the process of obtaining the actual measurement map including the two objects is a known technique in the process such as the route search, detailed description thereof is omitted here.

  There is no limitation on the timing at which the map acquisition unit 108 acquires the actual measurement map. For example, when there are a plurality of deformed maps acquired individually by the deformed map acquiring unit 103 and individually including objects corresponding to two or more object identifying information received by the object identifying information receiving unit 102, the plurality of deformed maps When one of the deformed maps is selected by an instruction from a user or the like via a reception unit (not shown), and the deformed maps each including objects corresponding to two or more object identification information are determined one by one. An actual measurement map may be acquired. In addition, an arbitrary set of deformed maps acquired individually by the deformed map acquiring unit 103 and individually including objects corresponding to two or more object identifying information received by the object identifying information receiving unit 102, or as will be described later. You may make it acquire an actual measurement map about the group of the highest ranking.

  The map acquisition unit 108 acquires the coordinates of the vertices of the MBR in the real space for each of the two or more deformed maps, like the attribute value acquisition unit 105, and among the vertices corresponding to the respective deformed maps. An actual measurement map including both coordinates of the nearest vertex may be acquired. Further, the map acquisition unit 108 performs a so-called route search on the actual map using the coordinates in the real space of the object indicated by the two or more object identification information received by the object identification information reception unit 102, and obtained by the route search. An actual measurement map that passes through the route and that excludes a portion that overlaps the MBR region of the real space corresponding to the deformed map may be acquired. Or you may acquire the actual measurement map also including the part which overlaps with the area | region of MBR of the real space corresponding to a deformed map. You may acquire the actual measurement map which excluded the range shown by MBR from the actual measurement map containing the range shown by MBR corresponding to a deformed map.

  The map acquisition unit 108 can be usually realized by an MPU, a memory, or the like. The processing procedure of the map acquisition unit 108 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The scale acquisition unit 109 obtains position information on the deformed map corresponding to two or more object identification information corresponding to two or more objects on the deformed map acquired by the deformed map acquiring unit 103 and position information in the real space, respectively. Acquire the scale of the deformed map using the acquired location information. For example, the scale acquisition unit 109 uses the object identification information of two or more objects corresponding to the deformed map acquired by the deformed map acquisition unit 103, and position information (for example, coordinates) of the two or more objects on the deformed map, The position information (for example, coordinates) in the real space is acquired, the distance on the deformed map between the two or more points indicated by the two or more objects and the distance in the real space are acquired, and the acquired distance is used. Then, the scale of the deformed map is obtained by calculating the distance ratio. Further, the scale may be calculated from the area ratio between the area of the MBR including the objects on the deformed map and the area of the MBR including the positions of these objects in the real space. The scale here may be considered similar to the scale that is one of the attribute values described above. The process in which the scale acquisition unit 109 acquires the scale is the same as the process in which the attribute value acquisition unit 105 acquires the scale attribute value for the deformed map, and detailed description thereof is omitted here. Note that the scale acquisition unit 109 and the part that performs the process of acquiring the scale of the attribute value acquisition unit 105 may be realized by a single processing unit.

  The scale acquisition unit 109 can usually be realized by an MPU, a memory, or the like. The processing procedure of the scale acquisition unit 109 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 110 outputs the deformed map acquired by the deformed map acquiring unit 103. Here, it is considered that the output unit 110 also obtains the deformed map by outputting link information for the deformed map or outputting an image obtained by enlarging or reducing the deformed map.

  When the deformed map acquisition unit 103 acquires two or more deformed maps, the output unit 110 may individually output two or more deformed maps, for example. For example, when the output is a display, two or more deformed maps may be simply displayed side by side in one screen. Two or more deformed maps may be output as two screens that can be switched.

  For example, the output unit 110 may combine and output two or more deformed maps when the deformed map acquiring unit 103 acquires two or more deformed maps. The combination here is, for example, combining deformed maps and outputting them as one deformed map (for example, one image). For example, the output unit 110 generates and outputs an image in which two or more deformed maps are arranged and combined. When combining, for example, the ends to be combined may be overlapped by a predetermined width. Further, matching objects on two or more deformed maps may be detected, and an image in which the deformed maps are overlapped and combined so that one or more matching objects overlap each other may be output. The matching object can be acquired by searching for an object with the corresponding object identification information matching, for example. When selecting overlapping objects among matching objects, for example, when the deformed maps are overlapped, the distance between the objects indicated by the two or more object identification information received by the object identification information receiving unit 102 is closest. Select such an object. When overlapping images, the overlapping images can be combined in any combination mode (for example, multiplication mode, screen mode, etc.), and how the transparency of the overlapping images can be changed. You may do it. In the case of superimposing, it is preferable that the superimposed vertical relationship can be appropriately switched according to the operation of the user or the like.

  When the output unit 110 outputs two or more deformed maps side by side or outputs the combined maps, the output unit 110 arranges the positions of the objects in the real space indicated by the object identification information received by the object identification information receiving unit 102; It is preferable to arrange the deformed maps so that the arranged order of the deformed maps including this object is the same arranged order. Similarly, the direction in which the deformed maps are arranged is the same as the direction in which the objects indicated by the object identification information received by the object identification information receiving unit 102 are arranged, and the direction in which the deformed maps in which these objects are arranged are arranged in the same direction. It is preferable to arrange them as follows. A button for acquiring (such as displaying) another deformed map at the end of the direction in which the other deformed map is arranged in one deformed map by acquiring information on the order and direction of the arrangement. Etc. may be arranged.

  The output unit 110 may further output the actual measurement map acquired by the map acquisition unit 108. For example, the output unit 110 may display the measured map side by side with the deformed map, as in the case of outputting two deformed maps. Similarly to the case of two deformed maps, the deformed map and the actually measured map may be combined and output. Further, when combining, the coordinates in the real space in the area indicated by the actual measurement map acquired by the map acquisition unit 108 and the coordinates in the real space corresponding to one object on one deformed map are the map acquisition unit 108. It is determined whether or not the coordinates in the real space area indicated by the actual measurement map acquired, and if it is determined that the coordinates are in the real space area, one object on the deformed map is placed. The position corresponding to this object on the actual measurement map may be overlapped and combined. When there are a plurality of objects on the deformed map corresponding to the coordinates in the real space area indicated by the actual measurement map, for example, the object indicated by the object identification information received by the object identification information receiving unit 102 or the position closest to this object May be detected as an object indicating the position to be superimposed. Here, the closest position may be the closest position on the deformed map or the closest position in the real space.

  The output unit 110 may output the deformed map so that the deformed map associated with the type information received by the object identification information receiving unit 102 is ranked higher. For example, the output unit 110 acquires type information corresponding to each deformed map acquired by the deformed map acquiring unit 103 from the deformed map information storage unit 101 by searching or the like, and the type information acquired for each deformed map is an object identification. The information receiving unit 102 determines whether the type information matches the received type information, the deformed map associated with the matching type information is ranked higher, and the deformed map associated with the mismatched type information is left as it is. Or rank lower. The score used for determining the ranking is managed in association with each of the deformed maps acquired by the deformed map acquiring unit 103, and the ranking of the deformed map determined to match the type information is shown. The score may be increased by a predetermined value. And finally, you may determine the ranking at the time of outputting a deformed map in an order from this score high. Note that this score may be increased or decreased by other factors.

  To output the deformed map so that the deformed map associated with the received type information is ranked higher, for example, the deformed map associated with the received type information is more than the deformed map that is not associated, It may be output (for example, displayed) so as to be arranged on the upper side (front side), or a deformed map associated with the received type information is compared with a deformed map not associated with the received type information. Alternatively, a higher ranking value may be output in association with each other. That is, the output of the deformed map may be, for example, an output in ranking order, or may be an output in which the deformed map is associated with the ranking value of the deformed map.

  The output unit 110 may rank and output the deformed map according to one or more attribute values of the deformed map. For example, the default map may be output so that the MBR in the real space including the object on the deformed map is ranked higher in descending order. Note that, as described above, after ranking by type information, only those having the same ranking may be ranked by such attribute values to output a deformed map.

  The output unit 110 may output the one or more deformed maps acquired by the deformed map acquiring unit 103 in association with the scale. The output unit 110 may output the scales in association with each other. For example, the output unit 110 may output information having a deformed map and a scale, or may be output on the deformed map acquired by the deformed map acquisition unit 103. It is also possible to output a deformed map obtained by arranging or combining characters or images indicating

  The output of the deformed map of the output unit 110 may be considered to include the output of link information associated with the deformed map. Further, for example, when outputting (for example, displaying) a plurality of deformed maps on one screen, the plurality of deformed maps are reduced and displayed, and the default map is reduced by the operation of the mouse or the touch panel by the user or the like. This default map may be output (for example, displayed) in its original size or enlarged size.

  Output here means display on a display, projection using a projector, printing on a printer, transmission to an external device, storage on a recording medium, processing results to other processing devices or other programs, etc. It is a concept that includes delivery. The output unit 110 may or may not include an output device such as a display. The output unit 110 can be implemented by output device driver software, or output device driver software and an output device.

  FIG. 3 is a flowchart showing the operation of the map information processing apparatus 1. The operation will be described below with reference to the flowchart of FIG. Here, as an example, a case where the deformed map receiving unit 104 receives a deformed map in which the object identification information of the raster (bitmap) image is combined as an object on the map will be described as an example.

  (Step S101) The object identification information receiving unit 102 determines whether two or more pieces of object identification information have been received. If accepted, the process proceeds to step S102. If not accepted, the process proceeds to step S104.

  (Step S102) The object identification information receiving unit 102 determines whether type information has been received. If accepted, the process proceeds to step S103, and if not accepted, the process returns to step S102.

  (Step S103) The map information processing apparatus 1 performs a process of outputting a deformed map using the two or more pieces of object identification information received in step S101 and the type information received in step S102. Details of this processing will be described later. Then, the process returns to step S101.

  (Step S104) The deformed map receiving unit 104 determines whether or not one or more deformed maps and object identification information corresponding to objects displayed on the deformed maps have been received. Here, as an example, it is determined whether or not one or more deformed maps including object identification information have been received. If accepted, the process proceeds to step S105. If not accepted, the process returns to step S101.

  (Step S105) The deformed map receiving unit 104 extracts object identification information included in each of the one or more deformed maps received in step S104. For example, an image of one or more characters on the deformed map is recognized as a character string using a character recognition technique such as OCR, and the recognized character string is acquired as object identification information. The extracted object identification information is temporarily stored in a storage medium (not shown) or the like in association with the deformation map that is the extraction source.

  (Step S106) The map information processing apparatus 1 performs a process of acquiring the deformed map information using the deformed map received in step S104, the object identification information or the position information acquired in step S105, and the like. Details of this processing will be described later. Then, the process returns to step S101.

  In the flowchart of FIG. 3, the process ends when the power is turned off or the process is terminated.

  FIG. 4 is a flowchart showing details of the process of outputting the deformed map by the map information processing apparatus 1, and shows the process corresponding to step S103 of FIG. The details of the process of outputting the deformed map will be described below.

  (Step S201) The deformed map acquisition unit 103 substitutes 1 for a counter m.

  (Step S202) The deformed map acquisition unit 103 determines whether or not there is m-th object identification information in the object identification information received in step S101. If there is, the process proceeds to step S203, and if not, the process proceeds to step S208.

  (Step S <b> 203) The deformed map acquisition unit 103 performs a search using the mth object identification information as a search key, and the deformed map stored in the deformed map information storage unit 101 corresponds to the mth object identification information. Detect from map information.

  (Step S204) The deformed map acquisition unit 103 determines whether one or more deformed maps have been detected. If it can be detected, the process proceeds to step S205, and if it cannot be detected, the process proceeds to step S207.

  (Step S205) The deformed map acquisition unit 103 temporarily stores one or more deformed maps detected in step S203 in a storage medium (not shown) or the like. Then, the process proceeds to step S206.

  (Step S206) The deformed map acquisition unit 103 increments the value of the counter n by 1. Then, the process returns to step S202.

  (Step S207) The deformed map acquisition unit 103 outputs an error indicating that two or more deformed maps that include all the objects corresponding to the two or more object identification information received in step S101 cannot be acquired. Return to the upper process.

  (Step S208) The deformed map acquisition unit 103 determines whether there is a deformed map corresponding to all of the two or more pieces of object identification information received in step S101. Specifically, it is determined whether or not there is a common deformed map among the deformed maps searched by the deformed map acquisition unit 103 for each object identification information. For example, in a deformed map searched for by object identification information, if there is a common deformed map file name, the deformed map with this file name is a common deformed map. If there is a common deformed map, the process proceeds to step S209, and if not, the process proceeds to step S213.

  (Step S209) The output unit 110 performs ranking using the type information received in step S102 for the deformed map determined to be a deformed map corresponding to all of the two or more object identification information in step S208, The deformed map is output so that the deformed map associated with the received type information is ranked higher. Specifically, a deformed map associated with the type information received in step S102 is detected from the deformed map determined to correspond to all of the two or more object identification information in step S208, and the detected deformed map is detected. The map is ranked so as to be higher than the deformed map that has not been detected, and the deformed map is output. The determination as to whether or not one deformed map is the deformed map associated with the type information received in step S102 is, for example, storing the type information corresponding to the one deformed map in the deformed map information storage unit 101. It can be determined by searching from the deformed map information that has been obtained, and determining whether the acquired type information matches the type information received in step S102. For example, if they match, the one deformed map indicates that it is a deformed map associated with the type information received in step S102, and if it does not match, it indicates that the deformed map is not a correlated deformed map. Become. The output unit 110 outputs (for example, displays) a preview image of a deformed map, an icon, a file name, and the like in an order corresponding to the ranking, for example.

  (Step S <b> 210) The output unit 110 determines whether one of the deformed maps displayed by ranking is selected via a not-shown receiving unit or the like. If it is selected, the process proceeds to step S211, and if it is not selected, the process returns to step S210.

  (Step S211) The scale acquisition unit 109 acquires a scale for the deformed map selected in step S210. For example, using the object identification information extracted in step S105, using geocoding or the like, the real space coordinates of all objects on the deformed map are obtained, and the MBR area in the real space is obtained using this. To do. In addition, the coordinates of all the objects on the deformed map are acquired by acquiring the information on the position where the object is recognized when extracting the object identification information in step S105 as the coordinates of each object on the deformed map. To do. And the area of MBR on a deformed map is acquired using this. Then, the scale of the deformed map is acquired from the area ratio between the real space and the MBR on the deformed map. Here, “all objects” may be considered as “all objects satisfying a predesignated condition”. For example, “all objects” may be “all objects excluding objects corresponding to object identification information designated in advance”.

  (Step S212) The output unit 110 outputs the deformed map selected in Step S210 and the scale acquired in Step S211. For example, the output unit 110 displays an image in which a scale is arranged on a partial area of the deformed map. Then, the process returns to the upper process.

  (Step S213) The output unit 110 ranks the deformed map determined to be the deformed map corresponding to each object identification information in step S208 using the type information received in step S102 for each object identification information. The deformed map is output for each object identification information so that the deformed map associated with the received type information is ranked higher. The ranking process is the same as that in step S209.

  (Step S214) The output unit 110 determines whether a deformed map has been selected one by one in each of the deformed maps displayed by ranking according to two or more object identification information via a receiving unit (not shown). To do. That is, it is determined whether or not the deformed map is selected one by one for each object identification information. If it is selected, the process proceeds to step S215. If it is not selected, the process returns to step S214.

  (Step S215) The determination unit 107 determines whether or not the distance between the two or more deformed maps selected in step S214 is long. For example, it is determined whether there is common object identification information among the object identification information respectively associated with two or more deformed maps. If there is common object identification information, between real space areas indicated by the deformed map, Since it is considered that there is an overlapping part, it is determined that the distance is not far, and if there is nothing in common, it is determined that the distance is long. If it is determined that it is far, the process proceeds to step S216. If it is determined that it is not far, the process proceeds to step S217.

  (Step S216) The map acquisition unit 108 acquires an actual measurement map including at least one object among the objects displayed on each of the two or more deformed maps. For example, one or more pieces of object identification information are acquired from the object identification information associated with each deformed map, and an actual measurement map (not shown) possessed by the WEB or the map information processing apparatus 1 is obtained as an actual measurement map including all the acquired objects. Obtain from a map database.

  (Step S217) The scale acquisition unit 109 acquires a scale for each deformed map selected in step S214. The process of acquiring the scale from each deformed map is the same as the process of step S211.

  (Step S218) The output unit 110 combines two or more deformed maps selected in step S214. In step S216, when an actual map is acquired, two or more deformed maps and the actual map are combined.

  (Step S219) The output unit 110 outputs the map obtained by combining in step S218 and a plurality of scales acquired for each deformed map in step S217. Each scale is preferably arranged on an area derived from a deformed map from which the scales are acquired or in the vicinity thereof. Then, the process returns to the upper process.

  In the flowchart of FIG. 4, the process ends when the power is turned off or the process ends.

  FIG. 5 is a flowchart showing details of the process of acquiring deformed map information by the map information processing apparatus 1, and shows a process corresponding to step S106 of FIG. Hereinafter, details of the process of acquiring the deformed map information will be described. Here, a case where the classification unit 106 acquires the type information of the deformed map using the determination information obtained by machine learning will be described as an example.

  (Step S301) The attribute value acquisition unit 105 substitutes 1 for a counter n.

  (Step S302) The attribute value acquisition unit 105 determines whether or not the nth deformed map is present in the one or more deformed maps received in step S104. If there is, the process proceeds to step S303, and if not, the process proceeds to step S315.

  (Step S303) The attribute value acquisition unit 105 assigns 1 to the counter k.

  (Step S304) The attribute value acquisition unit 105 acquires, for the nth deformed map, the kth attribute value among a plurality of attribute values designated in advance to be acquired from the deformed map. For example, the kth attribute value is acquired using the nth deformed map and one or more pieces of object identification information corresponding to the deformed map. In addition, when acquiring attribute values, information such as geocoding is obtained from a database (not shown) in the map information processing apparatus 1 or another apparatus capable of communicating via a network such as the Internet as necessary. The acquired information may be used to acquire the attribute value. Then, the acquired attribute value is stored in a storage medium (not shown) in association with the nth deformed map. The plurality of attribute values that are specified to be acquired in advance are, for example, logical sums of attribute values that are respectively used as teacher data when acquiring different types of determination information to be described later.

  (Step S305) The attribute value acquisition unit 105 increments the value of the counter k by 1.

  (Step S306) The attribute value acquisition unit 105 determines whether or not there is a k-th attribute value among a plurality of attribute values designated in advance to be acquired from the deformed map. If there is, the process returns to step S304, and if not, the process proceeds to step S307.

  (Step S307) The classification unit 106 reads out determination information about the route map stored in advance in the determination information storage unit 1061. In addition, among the attribute values acquired for the nth deformed map, two or more attribute values specified in advance corresponding to the determination information are read from the storage medium in which the attribute values are stored in step S304. And it is determined whether it is a route map using the read attribute value and the determination information about a route map. The two or more attribute values specified in advance here are two or more attribute values given as teacher data in the machine learning for the route map.

  (Step S308) The classification unit 106 determines whether or not the route map is determined by the determination in step S307. If it is determined that the map is a route map, the process proceeds to step S309. If it is not determined, the process proceeds to step S311.

  (Step S309) The classification unit 106 acquires type information (for example, “route map”) indicating that it is a route map, and stores the type information in a storage medium (not shown) in association with the nth deformed map.

  (Step S310) The attribute value acquisition unit 105 increments the value of the counter n by 1. Then, the process returns to step S302.

  (Step S311) The classification unit 106 reads out determination information about the object map stored in advance in the determination information storage unit 1061. In addition, among the attribute values acquired for the nth deformed map, two or more attribute values specified in advance corresponding to the determination information are read from the storage medium in which the attribute values are stored in step S304. Then, using the read attribute value and determination information about the object map, it is determined whether or not it is an object map. The two or more attribute values specified in advance here are two or more attribute values given as teacher data in the machine learning for the object map.

  (Step S312) The classification unit 106 determines whether or not the object map is determined by the determination in step S311. If it is determined to be an object map, the process proceeds to step S313, and if it is not determined, the process proceeds to step S314.

  (Step S313) The classification unit 106 acquires type information (for example, “object map”) indicating that it is an object map, and stores the type information in a storage medium (not shown) or the like in association with the nth deformed map.

  (Step S314) The classification unit 106 acquires type information indicating that it is a mixed map (for example, “mixed map”), and stores it in a storage medium (not shown) in association with the nth deformed map.

  (Step S315) The classification unit 106 receives each deformed map received in step S104, one or more pieces of object identification information extracted from each deformed map in step S105, and each deformed map in step S309, step S313, or step S314. Is stored in the deformed map information storage unit 101. Then, the process returns to the upper process.

  In the flowchart of FIG. 5, one of “route map”, “object map”, and “mixed map” is selected as the type information of the deformed map, but other type information is acquired. You may make it do. For example, deformed maps classified into types other than the above are given as teacher data together with attribute values to be machine-learned, and the determination information obtained by the learning is used, so that the deformed map type information other than the above three It is also possible to classify them into the following types.

  In addition, here, the case where type information is acquired using teacher learning has been described as an example. However, as described above, by using rules specified in advance, peer objects described later, etc. The type information may be acquired.

  Further, in the flowchart of FIG. 5, in step S315, the deformed map information in which each deformed map, one or more object identification information extracted from each deformed map, and the type information acquired for each deformed map are associated with each other. The information is stored in the deformed map information storage unit 101. Instead of this step S315, every time the type information is acquired for the nth deformed map in step S309, step S313, or step S314, the nth deformed map is displayed. The deformed map information in which the one or more object identification information extracted from the nth deformed map and the type information acquired for the nth deformed map are associated with each other in the deformed map information storage unit 101 sequentially. Anyway .

  In the flowchart of FIG. 5, the process ends when the power is turned off or the process ends.

  Hereinafter, a specific operation of the map information processing apparatus 1 in the present embodiment will be described with an example. Here, a case where there are two or more pieces of object identification information received by the object identification information receiving unit 102 will be described. However, in the present invention, three or more pieces of object identification information may be accepted. Also, the map shown in this specific example, the coordinates of the object on the deformed map, and the values of the coordinates in the real space are for convenience of explanation and are not necessarily accurate.

  FIG. 6 is a schematic diagram showing the map information processing apparatus 1 in this specific example. In this specific example, the map information processing apparatus 1 is assumed to be a portable navigation device. This navigation device 1 has a monitor 1101 and a touch panel is provided on the surface thereof. In addition, this navigation device can access a server device 100 equipped with a map database, a map search engine, etc. that can provide a measured map or provide geocoding through a wireless network line. Suppose that Instead of using the server device 100, a plurality of different server devices may be used for different purposes.

  FIG. 7 is a diagram illustrating an example of a deformed map information management table for managing the deformed map information stored in the deformed map information storage unit 101. The deformed map information management table has items of “deformed map”, “object identification information”, “coordinates”, and “type”. Here, it is assumed that each record of the deformed map information management table is deformed map information. Here, the “deformed map” is assumed to be the file name of the deformed map. “Object identification information” is object identification information of an object on the deformed map. “Object coordinates” are xy coordinates of the object indicated by the object identification information. “Type” is type information of the deformed map. As the type information of the deformed map, it is assumed here that there are at least three types of type information: an object map, a route map, and a mixed map. Needless to say, the object identification information and the type information may be managed using another management table having items of “object identification information” and “type”.

  FIG. 8 is a diagram illustrating an example of an input screen that is an interface screen for receiving two or more pieces of object identification information. Here, as an example, it is assumed that a field 81 and a field 82 for inputting two pieces of object identification information each indicating a departure place and a destination are provided. In addition, it is assumed that a selection button 83 for inputting type information of the deformed map is provided. When three or more pieces of object identification information are received, for example, a field for receiving one or more waypoints may be provided.

  First, the user displays the input screen shown in FIG. 8 for searching for a deformed map, and operates the touch panel to input object identification information “Kyoto Station” as a departure place into the field 81. It is assumed that the object identification information “Higashi Honganji” which is the destination is input to 82, the selection button 83 is pressed to select an object map, and then the search button 84 is pressed. In the field 81, object identification information such as a place name corresponding to the coordinates of the current position acquired by the GPS or the like may be input. For example, it is possible to acquire a place name or the like of the current position corresponding to the coordinates or the like of the current position acquired by the GPS or the like by geocoding technology. The field 81 may not be displayed, and object identification information such as a place name automatically acquired using GPS may be input.

  When the search button 84 is pressed, the object identification information receiving unit 102 receives two pieces of object identification information “Kyoto Station”, “Higashi Honganji”, and type information “object map”. Then, the deformed map acquisition unit 103 starts searching for the deformed map.

  First, the deformed map acquisition unit 103 sets “Kyoto Station” in which the value of “object identification information” is one object identification information received by the object identification information receiving unit 102 in the deformed map information management table shown in FIG. All records that match (that is, deformed map information) are searched. Then, the value of the “deformed map” of each detected record is acquired as a deformed map corresponding to the object identification information “Kyoto Station”, and is temporarily stored in a storage medium such as a memory in association with the object identification information “Kyoto Station”. To do. Here, it is assumed that the deformed maps “kyosta01.jpg”, “kyosta02.jpg”, “kyomap01.jpg”, and “kyoaccess.jpg” are acquired and accumulated.

  Similarly, the deformed map acquisition unit 103 similarly acquires the corresponding deformed map in the object identification information “Higashi Honganji” received by the object identification information receiving unit 102, and the object identification information “Higashi Honganji” Accumulate and store. Here, it is assumed that the deformed maps “kyosta01.jpg”, “kyosta02.jpg”, “kyoaccess.jpg”, and “kyokanko.jpg” are acquired and accumulated.

  Next, the deformed map acquisition unit 103 searches for overlapping deformed maps between the deformed maps accumulated for each object identification information. Since the duplicate search process is known, a description thereof will be omitted. As a result of the duplicate search, it is assumed that deformed maps “kyosta01.jpg”, “kyosta02.jpg”, and “kyoaccess.jpg” are detected. The deformed map acquisition unit 103 acquires the detected deformed map and temporarily stores it in a storage medium (not shown).

  The output unit 110 is a type in which the object map information receiving unit 102 receives the detected deformed map because the deformed map acquiring unit 103 has detected one or more deformed maps corresponding to both of the two or more pieces of object identifying information. Ranking using information “object map”. Specifically, the deformed map acquired by the deformed map acquiring unit 103 is sequentially read, and a record (that is, the deformed map information) in which the value of “deformed map” matches the read-out deformed map in the deformed map information management table shown in FIG. ) And the value of “type” of the detected record matches the “object map”, the value indicating that the ranking is higher is associated with the read deformed map. If they do not match, a value indicating higher rank is not associated. The value indicating whether or not it is higher rank is, for example, flag information. For one deformed map, the record detection may be terminated when one record of the deformed map management table is detected.

  For example, in the three deformed maps acquired above, since the “type” corresponding to the deformed map “kyosta01.jpg” is “object map”, the deformed map “kyosta01.jpg” is higher in rank. Since the corresponding “type” is “route map”, the other deformed maps “kyosta02.jpg” and “kyoaccess.jpg” are not associated with the values shown.

  Then, the output unit 110 arranges the three deformed maps acquired above so that the deformed maps associated with the values indicating that the ranking is higher are arranged above the other deformed maps. Is displayed on the monitor 1101. However, here, without displaying the deformed map itself, a set of a reduced image of the deformed map and a file name is displayed side by side in descending order of ranking. Further, it is assumed that a function as a button for selecting a corresponding deformed map is set in each file name, like the link on the WEB page.

  FIG. 9 is a diagram showing a display example in which deformed maps corresponding to both of the two object identification information are ranked according to type information and output.

  It should be noted that, in the deformed maps associated with the value indicating superiority, or in the deformed maps not associated with the value indicating superiority, the display order or the like may be determined in any way. Good. For example, the order may be determined randomly, or the order may be determined in ascending order or descending order of the file names. Further, the order may be determined depending on the size of the MBR area in the real space that can be acquired in each deformed map, or the scale.

  Here, if the user operates the touch panel and presses the file name of the deformed map “kyosta01.jpg” at the top in the screen shown in FIG. 9, an unillustrated reception unit or object identification information reception unit 102 etc. receive the instruction | indication which displays this deformed map.

  Next, the scale acquisition unit 109 acquires the scale of the deformed map “kyosta01.jpg”. For example, the scale acquisition unit 109 acquires all “object identification information” and “object coordinates” corresponding to the deformed map “kyosta01.jpg” from the deformed map information management table shown in FIG. However, some object identification information specified in advance may be excluded.

  Then, the coordinates of the real space respectively corresponding to all the acquired “object identification information” are acquired using a so-called geocoding technique. Here, in response to the input of the object identification information such as the place name, the server device 100 that provides the service for outputting the latitude and longitude information in the real space of the object indicated by the object identification information is transmitted via the wireless network line. All the “object identification information” is transmitted, and the coordinates of the corresponding real space are acquired. For example, when the object identification information “Higashi Honganji” is transmitted, “latitude 34.9999 degrees, longitude 135.75847 degrees” is acquired as real space coordinates. Similarly, the coordinates of the real space are acquired for other object identification information. Since a server device that provides such a geocoding service, an API for performing geocoding, and the like are known, a description thereof is omitted here.

The scale acquisition unit 109 determines MBRs on the deformed map including all the “object coordinates” acquired above, and calculates the area thereof. For example, it is assumed that the MBR area of the deformed map calculated here is 100 cm ² . Further, the MBR including all the coordinates of the real space acquired as described above is determined for the object identification information, and the area is calculated. It is assumed that the MBR area of the real space calculated here is 640000 m ² .

  The scale acquisition unit 109 calculates an area ratio using the acquired area. The area ratio is 1/64000000. And 1/8000 which is the square root is acquired as a reduced scale.

  The output unit 110 generates an image in which the scale acquired by the scale acquisition unit 109 is arranged in the right corner, which is a predesignated position, on the image of the deformed map “kyosta01.jpg” and displays the image on the monitor 1101.

  FIG. 10 is a diagram illustrating a display example of a deformed map by the output unit 110.

  Here, it is assumed that the user inputs “Kiyomizu Temple” in the field 81 and “Yasaka Shrine” in the field 82 and sets the selection button 83 as the “object map” and presses the search button 84 on the input screen shown in FIG. Then, the object identification information receiving unit 102 receives two pieces of object identification information “Kiyomizu Temple”, “Yasaka Shrine”, and type information “object map”. Then, the deformed map acquisition unit 103 starts searching for the deformed map.

  First, the deformed map acquisition unit 103 performs the same processing as in the case of “Kyoto Station” described above, and is a deformed map corresponding to the object identification information “Kiyomizu Temple” from the deformed map information management table shown in FIG. “Kiomimizu01.jpg” and “kiomimizu02.jpg” are acquired and stored in a storage medium (not shown). Similarly, “yasaka01.jpg” and “yasaka02.jpg” are acquired and accumulated for the object identification information “Yasaka Shrine”.

  Next, similarly to the above, the deformed map acquisition unit 103 searches for overlapping deformed maps between the deformed maps accumulated for each object identification information. Here, it is assumed that no match is detected.

  Therefore, the output unit 110 ranks and displays the deformed map for each object identification information. Here, if the type information corresponding to “kiomimizu01.jpg” and “yasaka01.jpg” is “object map” and the type information of other deformed maps is “route map”, the received type information Is “object map”, so that “kiomimizu01.jpg” and “yasaka01.jpg” are determined as higher rankings, and the object identification information is arranged so that they are arranged above the other deformed maps. Separately, a reduced image of the deformed map and a file name of the deformed map are displayed side by side.

  FIG. 11 is a diagram illustrating a display example in which deformed maps corresponding to two pieces of object identification information are ranked according to type information and output.

  Here, it is assumed that the user operates the touch panel or the like to select “kiomimizu01.jpg” and “yasaka01.jpg” from the deformed map displayed for each two pieces of object identification information.

  FIG. 12 shows a deformed map “kiomimizu01.jpg” (FIG. 12A) and a deformed map “yasaka01.jpg” (FIG. 12B).

  The determination unit 107 acquires “kiomimizu01.jpg” and “yasaka01.jpg” which are the selected deformation maps, and determines whether or not the distance between the deformation maps is long. First, the determination unit 107 reads out object identification information corresponding to each acquired deformed map from the deformed map information management table shown in FIG. 7, and temporarily stores it in a memory or the like for each corresponding deformed map. For example, one or more records that match the values of each deformed map read and “deformed map” are detected from the deformed map information management table, and the “object identification information” of the detected record is acquired to correspond to each deformed map. Add and store temporarily. Then, duplicate object identification information is searched among the object identification information accumulated for each deformed map. When one or more duplicates are detected, the determination unit 107 determines that the distance between the deformed maps is not long. In addition, when no duplicate is detected, it is determined that the distance between the deformed maps is long. Here, since “Kodaiji” and “Yasaka-no-tou” are detected as overlapping object identification information, it is determined that the distance is not far.

  For this reason, the scale acquisition unit 109 acquires a scale for each deformed map as described above. Here, it is assumed that the scale acquired for “kiyomizu01.jpg” is “1/7000” and the scale acquired for “yasaka01.jpg” is “1/7500”.

  Since the output unit 110 determines that the distance between the two acquired deformed maps is not long, the output unit 110 combines the deformed maps. Specifically, for each of the object identification information “Kodaiji” and “Yasaka no To” determined to be duplicated by the determination unit 107, the coordinates of the real space are acquired using the server device 100 in the same manner as described above. Here, the coordinates of the real space are acquired in the same manner for the two pieces of object identification information “Kiyomizu Temple” and “Yasaka Shrine” received by the object identification information receiving unit 102. Then, for each of the two object identification information determined to be duplicated, the distance from the corresponding real space coordinate to the real space coordinate corresponding to each of the two object identification information received by the object identification information receiving unit 102 Is calculated. For example, for the overlapping object identification information “Kodaiji”, the distance from the real space coordinates of “Kodaiji” to the real space coordinates of “Kiyomizu Temple” and the real space coordinates of “Kodaiji” And the distance to the coordinates in the real space is calculated, and the total is calculated. The overlapping object identification information “Yasaka-no-tou” is calculated from the distance from the real-space coordinates of “Yasaka-no-tou” to the real-space coordinates of “Kiyomizu-dera” and the real-space coordinates of “Yasaka-no-tou”. The distance to the real space coordinates of “Yasaka Shrine” is calculated, and the total is calculated. Then, the totals are compared, and overlapping object identification information with a shorter total distance is acquired. Here, for example, it is assumed that the total distance acquired for “Kodaiji” is shorter. For this reason, the position of the object indicated by “Kodaiji” is determined as a reference position to superimpose two selected deformed maps, and the object identification information “Kodaiji” on each deformed map is determined. Get the position information of each object. Specifically, in the deformed map information management table shown in FIG. 7, a record in which “deformed map” is “kiomimizu01.jpg” and “object identification information” is “Kodaiji” is detected. The value (x11, y11) of “object coordinates” is acquired for the deformed map “kiomimizu01.jpg”. Similarly, a record in which the “deformation map” is “yasaka01.jpg” and the “object identification information” is “Kodaiji” is detected, and the value (x17, y17) of the “object coordinates” of the record is Acquired for the deformed map “yasaka01.jpg”.

  The output unit 110 then superimposes two deformed maps “kiomimizu01.jpg” and “yasaka01.jpg” so that the acquired coordinates (x11, y11) and (x17, y17) overlap each other. Join. Then, the combined image is displayed. When superimposing, for example, a deformed map “yasaka01.jpg” and “kiomimizu01.jpg” are displayed in a multiplication mode in a multiplication mode. The multiplication mode is a mode in which the color value obtained by multiplying the color values of both overlapping pixels is used as the color value of the combined pixels. The order in which the two are stacked does not matter. Further, the overlapped images may be combined in any way. For example, a portion of each deformed map that is overlapped with another deformed map may be combined so that the transparency increases continuously or stepwise as the center of the other deformed map is approached.

  In addition, here, as an example, the scale acquired by the scale acquisition unit 109 for one deformed map is the upper or lower side of the combined deformed map that is derived from the one deformed map and where the images do not overlap. Each is detected and arranged in the vicinity thereof.

  FIG. 13 is a diagram illustrating a deformed map output by combining the output unit 110.

  Here, it is assumed that the deformed maps selected by the user after displaying the screen displaying the ranked maps shown in FIG. 11 are “kiomizu02.jpg” and “yasaka02.jpg”.

  FIG. 14 shows a deformed map “kiomimizu02.jpg” (FIG. 14A) and a deformed map “yasaka02.jpg” (FIG. 14B).

  Then, as a result of determination by the determination unit 107, it is assumed that there is no overlapping object identification information between the two deformed maps and it is determined that the distance between the deformed maps is long.

  In this case, the map acquisition unit 108 acquires the object identification information corresponding to each deformed map from the deformed map information shown in FIG. 7 as in the case of acquiring the MBR described above, and the real space corresponding to each of the deformed maps is acquired. Get the coordinates. Then, the distance between the coordinates in the real space corresponding to the extracted object identification information is calculated for all combinations in the case of extracting the object identification information corresponding to each one from different deformed maps. Then, a combination of object identification information with the shortest distance is detected. For example, the two deformed maps “kiomimizu02.jpg” and “yasaka02.jpg” correspond to the object identification information “Sannizaka” corresponding to the deformed map “kiomimizu02.jpg” and the deformed map “yasaka02.jpg”. It is assumed that a combination with the object identification information “Kyoto Tsukimimachi Post Office” is detected as a combination of object identification information with the shortest real space distance.

  The map acquisition unit 108 wirelessly displays an actual measurement map including the two points indicated by the two objects corresponding to the two object identification information “Sannizaka” and “Kyoto Tsukimimachi Post Office” acquired in this manner. Obtained from the server apparatus 100 via the network line. Specifically, the map acquisition unit 108 transmits two pieces of object identification information “Sanninzaka” and “Kyoto Tsukimimachi Post Office” to the server device 100. When the server apparatus 100 receives the two object identification information transmitted from the map acquisition unit 108, the server apparatus 100 acquires an actual measurement map including the two points indicated by the two object identification information from the actual measurement map database or the like, It transmits to the map acquisition part 108. In addition, since the structure etc. which acquire the measurement map containing the two points which the two place names etc. which were input enter in what is called a route search between two points are well-known techniques, detailed description is carried out here. Is omitted.

  FIG. 15 is a diagram showing an actual measurement map acquired by the map acquisition unit 108 in this way.

  Then, the scale acquisition unit 109 acquires the scales for the two deformed maps.

  The output unit 110 inserts the actual map acquired by the map information acquisition unit 108 between the two deformed maps on the side where the object corresponding to the two object identification information acquired above is located, And the actual map. A scale is arranged on each deformed map. Then, a map obtained by combining is displayed on the monitor 1101.

  FIG. 16 is a diagram illustrating a display example of a map obtained by combining.

  Note that the actual measurement map can be considered as a real space area reduced and displayed almost accurately. Therefore, when the map acquisition unit 108 acquires the actual measurement map, it corresponds to the two pieces of object identification information acquired above. The real space coordinates and the information (for example, diagonal coordinates) that indicates the position and size of the actual map to be acquired are further acquired. The coordinates of the two object identification information on the acquired actual map are acquired from the positional relationship between the coordinates of the real space corresponding to the two object identification information, respectively, and the acquired coordinates on the actual map and the two deformed maps are acquired. The deformed map and the measured map may be overlapped and combined so that the coordinates indicated by the same object identification information above overlap each other.

  Here, the process etc. which acquire deformed map information using a deformed map and accumulate | store in the deformed map information storage part 101 are demonstrated.

  First, a plurality of deformed maps are acquired in advance by crawling or the like. Here, it is assumed that the deformed map is image information in which object identification information including a raster (bitmap) image or an outline image is combined and embedded as an object in the deformed map. Then, the deformed map is shown to one or more evaluators, and the evaluator evaluates whether each deformed map is an object map, a route map, or a mixed map. Then, the type information indicating the evaluation result is stored in a storage medium (not shown) in association with the deformed map. For example, a deformed map determined to be an object map is stored in association with type information “object map”.

  In addition, when the deformed map receiving unit 104 reads the same deformed map accumulated in the recording medium or the like, the deformed map receiving unit 104 performs one or more characters from each deformed map by performing character recognition processing such as OCR. The object identification information is extracted. The extracted object identification information and the coordinates on the deformed map where the object identification information is recognized are stored in a storage medium (not shown) in association with the deformed map. Here, the coordinates on the deformed map where the object identification information is recognized are considered as the coordinates of the object corresponding to the object identification information.

  The attribute value acquisition unit 105 extracts each deformation map using the object identification information extracted from each deformation map by the deformation map reception unit 104 and stored in association with the deformation map, and the coordinates of the object corresponding to the object identification information. Each time, a plurality of attribute values are acquired. This attribute value is, for example, an attribute value including two or more attribute values among the attribute values shown in FIG. 2A and FIG. In addition, the server device 100 or the like may be used as appropriate for acquiring the attribute value. The attribute value acquisition unit 105 stores the acquired two or more attribute values in association with the deformed map.

  Next, the classification unit 106 performs machine learning for each type information. Specifically, when performing machine learning on an object map, the type information acquired above and a plurality of attribute values are read for each deformed map information, and the object map type information is converted into evaluation data indicating that it is an object map. The type information other than the object map is converted into evaluation data indicating that it is not an object map. The converted type information is used as teacher data indicating evaluation for each deformed map type, and a plurality of attribute values are used as teacher data indicating features for each deformed map, and machine learning is performed for the object map. The determination information obtained by machine learning is accumulated in a determination information storage unit 1061 described later. In this way, determination information used when evaluating whether or not an arbitrary deformed map type is an object map is acquired.

  The same machine learning is also performed on the route map, and determination information used when evaluating whether or not an arbitrary deformed map type is the route map is acquired.

  Next, a deformed map used for acquiring deformed map information is prepared and given to the deformed map receiving unit 104. The given deformed map is, for example, a deformed map having object identification information combined on the map as a raster (bitmap) image. The deformed map receiving unit 104 extracts one or more pieces of object identification information from the deformed map by performing character recognition processing such as OCR. The extracted object identification information and the coordinates on the deformed map where the object identification information is recognized are stored in a storage medium (not shown) in association with the deformed map.

  Next, the attribute value acquisition unit 105 acquires a plurality of attribute values using the object identification information extracted from the deformed map received by the deformed map receiving unit 104 in the same manner as described above. Then, the classification unit 106 uses the acquired attribute values and the determination information acquired above to determine whether or not the deformed map type is an object map. The determination result of whether or not it is a map is output. If it is determined to be an object map, the attribute value acquisition unit 105 acquires type information “object map”.

  If it is determined that the map is not an object map, the classification unit 106 uses the plurality of attribute values acquired above and the determination information acquired above to determine whether the deformed map is a route map. It is used to determine whether or not the deformed map type is a route map. As a result of the determination, if it is determined that the map is a route map, the attribute value acquisition unit 105 acquires type information “route map”.

  If it is determined that the map is not a route map, the classification unit 106 determines that the deformed map is a mixed map and acquires type information “mixed map”.

  Then, the classification unit 106 acquires type information corresponding to the determination result using the determination information.

  When there are a plurality of deformed maps received by the deformed map receiving unit 104, the classification unit 106 may repeat the above processing.

  The classification unit 106 includes a deformed map received by the deformed map receiving unit 104, object identification information acquired for the deformed map, information indicating an object position indicated by the object identification information, and type information acquired for the deformed map. Are stored in the deformed map information storage unit 101 as deformed map information. Each of these information corresponds to information of each item of the above-described deformed map information. Such processing is repeated for the deformed map for which the deformed map information is to be acquired.

  As described above, according to the present embodiment, one deformed map corresponding to two or more object identification information received by the object identification information receiving unit, or two or more deformed maps respectively corresponding to the two or more object identification information. Can be output at least. Accordingly, for example, if there is a deformed map including two or more points indicated by two or more object identification information, this is output, and if there is no deformed map including two or more points, a deformed map including each point is displayed. , At least can be output, and a deformed map indicating two or more points can be appropriately output.

  In the above embodiment, the output unit 110 ranks and outputs one or more deformed maps once acquired by the deformed map acquiring unit 103, and the deformed map designated by the user is selected from the deformed maps. Although an example of outputting a map again has been described, in the present invention, one or more points including two or more points indicated by two or more object identification information from one or more deformed maps once acquired by the deformed map acquisition unit 103. The deformed map may be selected by the deformed map acquisition unit 103, and the output unit 110 may output the selected deformed map. When this deformed map is selected, it may be selected according to any rule. For example, a map having a high ranking may be selected or may be selected randomly. A deformed map having a large number of objects may be selected.

  Moreover, in the said embodiment, when it was judged that the distance of two acquired deformed maps is long, the case where the map acquisition part 108 acquires an actual measurement map was demonstrated. However, in the present invention, when it is determined that the distance is long, a route between two pieces of object identification information is acquired using a web or navigation system, and an actual measurement map such as a place name acquired on the route is acquired. The upper object identification information may be acquired, and a deformed map associated with the object identification information may be further acquired. Moreover, you may make it repeat such a process until deformed map information overlaps and continues.

(Embodiment 2)
In this embodiment, a guidance image for performing guidance for the deformed map acquired in the above embodiment is acquired.

FIG. 17 is a block diagram of the map information processing apparatus 2 in the present embodiment.
The map information processing apparatus 2 includes a deformed map information storage unit 101, an object identification information receiving unit 102, a deformed map acquiring unit 103, a deformed map receiving unit 104, a determining unit 107, a map acquiring unit 108, a scale acquiring unit 109, and an object classification unit. 201, a classification unit 202, a guide image acquisition unit 203, and an output unit 204.

  Configuration and operation of deformed map information storage unit 101, object identification information receiving unit 102, deformed map acquiring unit 103, deformed map receiving unit 104, attribute value acquiring unit 105, determining unit 107, map acquiring unit 108, scale acquiring unit 109, etc. Since is the same as that in the first embodiment, detailed description thereof is omitted here.

  The object classification unit 201 classifies objects on the deformed map into a path object that is an object indicating a road and a geographic object that is an object other than a road. The classification into the path object and the geographic object may be considered as obtaining the path object and the geographic object from the objects on the deformed map. Note that it is not necessary to classify all the objects on the deformed map into any one of these, and for example, objects that satisfy a predetermined condition may be excluded from classification targets. That is, some objects on the deformed map may be classified. For example, here, as an example, it is assumed that objects excluding objects indicating prefecture names, municipalities, mountain names, river names, and the like are classified. That is, the case where these objects are not included in the path object or the geographic object will be described as an example.

  The object classification unit 201 searches for a character string that matches the object identification information of each object on the deformed map, for example, in a database (not shown) in which a character string of a path prepared in advance is stored. If there is something, this object is determined as a path object. Further, an object that is not determined to be a path object is determined as a geographic object. This match may be a complete match or a partial match. This database may be included in the map information processing apparatus 2 or may be included in an external apparatus (not shown) accessible via a network or the like. Alternatively, one or more character strings (for example, roads, national roads, prefectural roads, highways, route lines, lines, railways, etc.) characteristic of the path are prepared in advance on a storage medium (not shown) as a clue phrase. The object identification information including the clue phrase at the beginning or end may be determined as the object identification information of the path.

  The object classification unit 201 detects a path object as described above, and instead of classifying the path object and the geographic object, the object classification unit 201 detects the geographic object and classifies an object other than the geographic object into the path object. Also good. For example, when a geographic object is detected, a character string that matches the object identification information of each object on the deformed map is searched in a previously prepared geographic object identification information database. Determine the object as a geographic object. Alternatively, geographic object identification information may be detected using a preliminarily prepared geographical object cue phrase, and an object corresponding to the detected geographic object identification information may be determined as a dust object.

  The object classification unit 201 can usually be realized by an MPU, a memory, or the like. The processing procedure of the guide image acquisition unit 203 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The classification unit 202 detects a peer object from the geographic objects classified by the object classification unit 201, and uses this peer object to group the geographic object and the path object according to the positional relationship. Acquires deformed map type information. Then, the deformed map information in which the acquired type information is associated with the deformed map and the object identification information is accumulated in the deformed map information storage unit 101. The peer object is an object having a peer relationship, for example, a similar object. A peer object is an object having a similar affiliation state with respect to a category among objects associated with one or more categories. The category is, for example, an object classification item, a tag attached to the object, or the like.

  The classification unit 202 is, for example, a database having a plurality of tree-structured categories prepared in advance, and using a database in which information about objects is stored in association with one or more categories, A peer object is detected from among the geographic objects classified by the object classification unit 201.

  For example, the classification unit 202 is a database having information about an object, and one or more categories to which information about each geographic object detected above belongs from a database in which the information about the object is associated with one or more categories. For each geographic object. For example, when such a database is a database in which a plurality of documents are stored, for example, a document in which the geographic object identification information detected by the object classification unit 201 is included in the title is searched and associated with the detected document. All or some of the categories may be acquired for each geographic object identification, and documents that contain geographic object identification information in parts other than titles (or parts including titles) are searched for and detected. All or some of the categories associated with the selected document may be acquired for each geographic object identification information. As the database as described above, for example, the map information processing apparatus 1 may have an internal database, or a database provided by an external apparatus via a network such as the Internet may be used. For example, Wikipedia (registered trademark) <URL: http://en.wikipedia.org/wiki/mainpage> can be used as such a database provided on the Internet or the like.

  The classification unit 202 determines whether or not two objects are similar to each other for a plurality of objects detected from the deformed map based on the similarity of the category to which the information about each object belongs. Specifically, the strength of the relationship between objects is expressed using a Jaccard coefficient. The definition of the Jaccard coefficient is shown below.

However, o ₁ and o ₂ indicate objects, and a category set to which the o ₁ and o ₂ objects belong is defined as C ₁ and C ₂ . If the value is equal to or greater than a predetermined threshold value, it is determined that the two objects are peers, and if the value is less than the threshold value, it is determined that the two objects are not peers. If there is a similar relationship between a plurality of objects, the objects are regarded as peers if the relationship is closed, but may be a peer between two objects if the relationship is not closed. The determination result of the peer object is accumulated in, for example, a storage medium (not shown).

  Then, the classification unit 202 acquires coordinates indicating the arrangement of the geographic object and the path object, and acquires a minimum spanning tree using the coordinates. The minimum spanning tree is a spanning tree in which the sum of the weights of the edges is minimum, and the algorithm used for creating the minimum spanning tree is a well-known technique, so the description thereof is omitted here. The information indicating the arrangement of the object used here may be position information on the deformed map or position information in the real space.

  Next, the classification unit 202 groups objects using the detection result of the peer object. Specifically, grouping is performed while following the path indicated by the minimum spanning tree in order from any object on the deformed map. At this time, the group is divided at the point in time when a geographic object having a peer relationship with the object that has been traced appears. That is, the group is divided so that two or more peer-related geographic objects are not included in one group. By doing this until all place names are traced, all objects on the deformed map are divided into groups. The classification unit 202 accumulates information having the object identification information and the group identification information of the group to which the object belongs in association with a storage medium (not shown).

  Next, the classification unit 202 acquires type information of the deformed map using the grouping result.

  Specifically, since an important element in the route map is the path, if the ratio of the number of objects in the group including the path object to the number of objects on the deformed map is greater than or equal to the threshold value, the default map is defined as the route map. Determine and obtain a route map as type information.

  In addition, since the element that is important in the object map is the object, if the number of groups divided by peer objects is greater than or equal to the threshold, or if it is not a route map, the deformed map is determined to be an object map and is used as type information. Get the object map.

  The important elements in the mixed map are both paths and objects. For this reason, when the conditions of the route map and the condition of the object map are satisfied, it is determined as a mixed map, and a mixed map is acquired as type information.

  The classification unit 202 can be usually realized by an MPU, a memory, or the like. The processing procedure of the classification unit 202 is usually realized by software, and the software is recorded on a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The guide image acquisition unit 203 acquires a guide image. The guide image is an image including an image acquired from the WEB at a point indicated by one or more objects on the deformed map in accordance with one or more deformed map types acquired by the deformed map acquiring unit 103. “Acquisition from WEB” means, for example, acquiring an image from a WEB server that provides an image of one or more points on the ground. The guide image acquisition unit 203 uses, for example, an image search engine or the like that provides an image of one or more points on the ground on the WEB, and acquires an image from the WEB on the point indicated by one or more objects on the deformed map. get. Further, an image of one or more points on the ground may be acquired by inputting a URL or the like designated in advance corresponding to one or more points on the ground. The guide image may be, for example, one or more images acquired from WEB, or a part thereof. Moreover, the image produced | generated using the 1 or more image acquired from WEB may be sufficient. For example, it may be an image obtained by combining a plurality of images acquired from WEB, and in this case, it may be considered that the acquired images are included. Further, the guidance image may be a moving image having images acquired for a plurality of points on the deformed map in association with the display order, for example. In this case, the acquired image may be considered as a frame image of a moving image. However, the moving image here may be one in which the constituent images are automatically and continuously displayed in the display order, or switched and displayed in the display order according to the user's operation. It may be a thing. For example, a moving image is a concept that includes a so-called slide show, a walk-through image, and the like. The moving image may further include images generated by the guide image acquisition unit 203 using images acquired from the WEB at a plurality of points. The generated image is, for example, an image for interpolating between images acquired from WEB or producing switching between images. In addition, when the guidance image which is a moving image is an image that can be automatically reproduced, the guidance image may include information for specifying timing such as time for switching each image. In this embodiment, a case where the guide image is such a moving image will be described as an example.

  The “image about a point indicated by one or more objects on the deformed map” is, for example, an image showing a landscape of the position (for example, coordinates) in the real space indicated by the object on the deformed map. Note that the position of the real space indicated by the object may be considered as the position of the real space indicated by the object identification information corresponding to the object. For example, this image is an image taken by a camera or the like at a position in the real space. In addition, this image may be a 2D or 3D image depicting a landscape at a position in the real space. This image may be a still image or a moving image. The guide image acquisition unit 203 searches for an image of a point indicated by one or more objects on the deformed map using, for example, a search engine on the WEB using object identification information corresponding to the deformed map as a search key. (Ie, by WEB search). In addition, using the object identification information corresponding to the deformed map, the coordinates of the real space are acquired by the above-described geocoding or the like, and the image at the position indicated by the coordinates is acquired by the WEB search. In addition, the process of providing an image showing the landscape corresponding to the position indicated by the object identification information such as the place name or the coordinates of the real space, the field of view at this position, and the like by WEB search, for example, as described in the related art Known in Street View and Location View, Patent Document 1 and the like. In such a WEB search, an image indicating the landscape of the input direction can be acquired by further inputting an arbitrary direction as a search key. In such a WEB search, the points where the image is provided are usually set to be scattered in the real space. For this reason, it is possible to change or designate the point from which the image is acquired with this point as a unit of movement.

  “According to the type, an image of one or more points indicated by one or more objects on the deformed map is acquired” means that, for example, one or more objects on the deformed map are selected according to the type. And acquiring an image of one or more points in the real space indicated by the selected object. The image of the point indicated by the object may be an image showing a landscape or the like in the position of the real space indicated by the object identification information corresponding to the object. From this coordinate, a rule corresponding to the type of the deformed map The image which shows the scenery etc. in the position moved according to may be sufficient. That is, the point indicated by the object may be considered as a peripheral region of one point in the real space. Moreover, the image which shows the landscape etc. of the direction determined according to the type of deformed map in these positions may be sufficient. Further, the guide image acquisition unit 203 may acquire images in the order according to the rule according to the type. The guide image acquisition unit 203 may acquire an image of a point indicated by the object by searching using object identification information corresponding to the object corresponding to one or more objects on the deformed map as a search key. In other words, it may be considered that the guidance image acquisition unit 203 determines what kind of image of the deformed map information and what kind of image is acquired, for example, based on the type information of the deformed map.

  Hereinafter, when the guidance image acquisition unit 203 acquires the guidance image from the deformed map according to the type, the type of the deformed image is any one of the route map, the object map, and the mixed map. Will be described as an example.

(A) When it is a route map When the type of the deformed map acquired by the deformed map acquisition unit 103 is a route map (for example, when the type information acquired from the deformed map information corresponding to the deformed map is a route map), The guide image acquisition unit 203 acquires, for example, an image related to one or more path objects on the deformed map. And the guidance image containing the acquired image is acquired. An image relating to a path object is, for example, an image of a landscape of a point in the real space indicated by the path object. Further, it may be an image of a landscape or the like of one or more points moved according to a rule specified in advance from a point in the real space indicated by the path object. Moreover, the image of the several points of the point vicinity of the real space which a path | pass object shows may be sufficient. Further, one or more images in a direction designated in advance may be used.

  For example, the guide image acquisition unit 203 uses at least one of the objects indicated by the two or more object identification information received by the object identification information receiving unit 102 on the deformed map as a starting point or an arriving point. Images related to the path object are sequentially acquired in an order determined at random or in an order determined by a rule designated in advance. And the guide image which is a moving image which made the acquisition order the display order is acquired using the acquired image. As a rule for determining the order of selecting path objects, for example, the minimum spanning tree as described above is used, and the path object of one deformed map or the path object and the geography object are joined together. There are rules such as determining the order of selection. Also, for example, in a minimum spanning tree composed of objects on a deformation map, when a geographic object and a geographic object are connected via one or more path objects, the rule for acquiring an image of a point indicated by the path object is also good. The rule may be any rule.

  In addition, the guide image acquisition unit 203 is configured to randomly determine the image of the point indicated by the path object between the objects indicated by the two or more object identification information received by the object identification information receiving unit 102, or in advance. You may acquire in the order determined by a rule.

  In addition, the guide image acquisition unit 203 performs a route search between points indicated by two or more object identification information received by the object identification information reception unit 102, and an image of a point indicated by a path object on the route indicated by the route search result To obtain a guide image. For example, the guide image acquisition unit 203 performs a route search between points indicated by two or more object identification information using a database of actual maps that the map information processing apparatus 1 has, an external device that can perform a route search, or the like. Get object identification information (measurement object identification information) on the actual map that shows stores, landmarks, scenic spots, buildings, place names, points, addresses, etc. that pass when moving between points, and information that indicates the passing order To do. Then, the detected actual object identification information and the object identification information of the object on the deformed map are detected as matching, and the image of the point indicated by the matching actual measurement object identification information has a corresponding passing order earlier. Get sequentially from the thing. And you may acquire the guidance image which is a moving image which made the acquisition order display order using this acquired image. In addition, since the technique which acquires the measurement object identification information on the path | route obtained by path | route search, and its passage order is well-known in the conventional path | route search technique etc., detailed description is abbreviate | omitted here. Further, the actually measured object identification information on the route may include object identification information such as a departure place and a target place.

(B) When it is an object map When the type of the deformed map acquired by the deformed map acquiring unit 103 is an object map (for example, when the type information acquired from the deformed map information corresponding to the deformed map is an object map), The guide image acquisition unit 203 acquires, for example, an image related to one or more geographic objects on the deformed map. And the guidance image containing the acquired image is acquired. The image related to the geographic object is the same as the image related to the path object described above except that the object is different.

  For example, the guide image acquisition unit 203 uses at least one of the objects indicated by the two or more object identification information received by the object identification information receiving unit 102 on the deformed map as a starting point or an arriving point. Images relating to geographic objects are sequentially acquired in an order determined at random or in an order determined by a rule specified in advance. And the guide image which is a moving image which made the acquisition order the display order is acquired using the acquired image. As a rule for determining the order in which geographic objects are selected, for example, a rule using a minimum spanning tree similar to the case of a path object can be used. However, the object map is often a tourist guide map or the like, and the point indicated by the object identification information received by the object identification information receiving unit 102 on the object map is merely a user's center of sightseeing or movement. In many cases, only the object to be used as a reference point at the time is indicated, so there are many cases of the starting point and the purpose, so the object specified by the object identification information received by the object identification information receiving unit 102, Setting to the start position, end position, etc. of the guide image may not be considered.

  Further, as described in the first embodiment as an example of the process for determining the type of deformed map, the guide image acquisition unit 203 classifies the objects on the deformed map into peer objects, and uses the peer objects. The order of selecting geographic objects for acquiring images may be determined. Information indicating the classification result of the peer object may be acquired from the classification unit 202 or the like, for example. First, images regarding objects that are in a peer relationship with the departure object are acquired in order. This order is, for example, an order along the order connected by the minimum spanning tree. Next, images relating to objects having a strong affinity with the departure object are acquired in order. The strong isotopic relationship is, for example, a geographic object whose Jaccard coefficient value used when detecting the isotopic relationship is less than a threshold value indicating that the isotopic relationship is present, but whose value is close to the threshold value. Specifically, the geographic object has a larger Jaccard coefficient value than a second threshold value that is specified in advance and is less than a threshold value that indicates a peer relationship. Finally, an image relating to an object having no peer relationship is randomly acquired. And the guide image which is a moving image which made the acquisition order the display order is acquired using the acquired image.

(3) When the map is a mixed map For example, when the type of deformed map acquired by the deformed map acquiring unit 103 is a mixed map, the guide image acquiring unit 203 (for example, acquired from deformed map information corresponding to the deformed map) When the type information is an object map), the guide image acquisition unit 203 acquires an image related to the geographic object on the deformed map for the closest path object on the deformed map. And the guidance image containing the acquired image is acquired.

  For example, the guide image acquisition unit 203 detects a path object closest to each geographic object on the deformed map. Then, the geographical objects having the same closest path object are grouped. Next, information regarding the objects in the group including the object corresponding to the object identification information that specifies the starting point of the two or more pieces of object identification information received by the object identification information receiving unit 102 is sequentially acquired. In the group, as described above, the order of acquisition may be determined using the peer relationship. When the acquisition of information related to the object is completed in one group, the same processing is sequentially performed for the other objects. And the guide image which is a moving image which made the acquisition order the display order is acquired using the acquired image. However, when there is an object corresponding to the object identification information for designating the destination in the deformed map, it is preferable to finally acquire an image from the group including this object. Further, when there is no object corresponding to the object identification information for designating the starting point in the deformed map, image acquisition may be started from any group.

  Note that the process of the guide image acquisition unit 203 that acquires a guide image from the deformed map for each type shown here is an example, and a guide image suitable for the type of deformed map may be acquired by other processes. Further, when the deformed map can be classified into a type other than the above, the guide image acquisition unit 203 may acquire the guide image from the deformed map by processing according to this type.

  In addition, when the guide image acquisition unit 203 acquires a guide image that is a moving image for each of the plurality of deformed maps, the guide image acquisition unit 203 may combine the acquired guide images into one guide image. When combining, it is preferable to combine so that the guide image of the deformed map in which the object on the starting point side is arranged is displayed first.

  The guide image acquisition unit 203 may acquire different images depending on the type of object. For example, different images may be acquired depending on whether the object is a path object or a geographic object. For example, when the object is a path object, only an image in one direction in the coordinates of the real space indicated by the object identification information is acquired. When the object is a geographic object, a plurality of surroundings in the coordinates of the real space indicated by the object identification information are acquired. An image may be acquired.

  The guide image acquisition unit 203 can be usually realized by an MPU, a memory, or the like. The processing procedure of the guide image acquisition unit 203 is usually realized by software, and the software is recorded in a recording medium such as a ROM. However, it may be realized by hardware (dedicated circuit).

  The output unit 204 outputs the deformed map acquired by the deformed map acquiring unit 103, similarly to the output unit 204 described in the first embodiment. Further, the output unit 204 outputs the guide image acquired by the guide image acquisition unit 203. The output unit 204 may display the guide image on the deformed map. For example, the output unit 204 may reproduce and display a guide image that is a moving image in response to an instruction from a user or the like received by a reception unit (not shown) or the like. Except for the configuration of the output unit 204 that outputs the guide image, the configuration is the same as the configuration of the output unit 110, and thus detailed description thereof is omitted here.

  FIG. 18 is a flowchart showing the operation of the map information processing apparatus 2. The operation will be described below with reference to the flowchart of FIG. In FIG. 18, the same reference numerals as those in FIG. 3 indicate the same or corresponding processes.

  (Step S401) The map information processing apparatus 2 performs a process of outputting a guidance image. Details of this processing will be described later. Then, the process returns to step S101.

  (Step S402) The map information processing apparatus 2 acquires deformed map information. Details of this processing will be described later. Then, the process returns to step S101.

  In the flowchart of FIG. 18, the process ends when the power is turned off or the process ends.

  FIG. 19 is a flowchart showing details of the operation of the map information processing apparatus 1 for outputting a guidance image. This process corresponds to step S401 in FIG. The operation will be described below with reference to the flowchart of FIG.

  (Step S501) The guide image acquisition unit 203 substitutes 1 for a counter p.

  (Step S502) The guide image acquisition unit 203 determines whether or not there is a p-th deformed map in the deformed map acquired by the deformed map acquiring unit 103. If there is, the process proceeds to step S503, and if not, the process proceeds to step S519.

  (Step S503) The guide image acquisition unit 203 classifies the objects on the p-th deformed map into path objects and map objects. Here, as an example, the object identification information corresponding to the object on the p-th deformed map is separated into the object identification information of the path object and the object identification information of the geographic object using a clue phrase or the like. The guide image acquisition unit 203 may acquire the classification result of the objects classified by the object classification unit 201 described later.

  (Step S504) The guide image acquisition unit 203 acquires type information of the p-th deformed map. For example, the type information received in step S102 may be acquired, or the type information corresponding to the p-th deformed map may be acquired from the deformed map information stored in the deformed map information storage unit 101.

  (Step S505) The guide image acquisition unit 203 determines whether or not the p-th deformed map is a route map. Specifically, it is determined whether or not the type information acquired in step S504 is type information indicating a route map. If it is a route map, the process proceeds to step S506, and if it is not a route map, the process proceeds to step S511.

  (Step S506) The guide image acquisition unit 203 performs a route search using the two or more object identification information received in Step S101, and acquires one or more measured object identification information on the searched route and its passing order. To do.

  (Step S507) The guide image acquisition unit 203 determines whether or not there is a match between the measured object identification information acquired in step S506 and the object identification information corresponding to the object. For example, if there is a match, the process proceeds to step S508, and if not, the process proceeds to step 509.

  (Step S508) The guide image acquisition unit 203 acquires images related to the objects determined to match in step S507 in order from the earliest passing order of the measured object identification information corresponding to the objects. Here, as an example, an image is acquired according to different rules depending on whether the object is a path object or a geographic object.

  (Step S509) The guidance image acquisition unit 203 uses the image acquired in step S508 (or step S515 or step S518) to acquire a guidance image that is a moving image in which the image acquisition order is the image display order. . The guide image is temporarily stored in a storage medium (not shown).

  (Step S510) The guide image acquisition unit 203 increments the value of the counter p by 1, and returns to step S502.

  (Step S511) The guide image acquisition unit 203 determines whether or not the p-th deformed map is an object map. Specifically, it is determined whether or not the type information acquired in step S504 is type information indicating an object map. If it is an object map, the process proceeds to step S512, and if it is not a route map, the process proceeds to step S516.

  (Step S512) The guidance image acquisition unit 203 applies the peer object corresponding to the object indicated by the object identification information received in Step S101 and the object indicated by the object identification information received in Step S101 from the geographic objects classified in Step S503. Detect objects with strong peer relationships. A specific example of detecting a peer object will be described later.

  (Step S513) The guide image acquisition unit 203 acquires the image of the peer object detected in Step S512. Here, for example, a minimum spanning tree is configured using objects on the p-th deformed map, and images of peer objects are sequentially acquired according to the order connected by the minimum spanning tree.

  (Step S514) The guide image acquisition unit 203 acquires an image of an object having a strong peer relationship detected in Step S512. Here, for example, as in the case of a peer object, images of objects having a strong peer relation are sequentially acquired in the order connected by the minimum spanning tree.

  (Step S515) The guide image acquisition unit 203 randomly acquires images of objects that are not detected in step S512 and have no peer relationship. Then, the process proceeds to step S509.

  (Step S516) The guide image acquisition unit 203 acquires the closest path object for each geographic object among the objects on the p-th deformed map. For example, the coordinates of the real space indicated by the object identification information corresponding to each object are acquired by geocoding or the like, and a path object having a short distance for each geographic object is acquired using the coordinates.

  (Step S517) The guidance image acquisition unit 203 detects a common path object for each geographic object for which a path object has been acquired in step S516, and groups the common objects together. For example, group identification information is associated with grouped geographic objects and stored in a storage medium (not shown).

  (Step S518) The guide image acquisition unit 203 sequentially acquires images related to the objects in each group grouped in step S517. For example, when there is a group including the object indicated by the object identification information received in step S101, acquisition of an image related to the object is started from this group, and when image acquisition for the objects in the group is completed, another group is sequentially Acquire images about. If there is no group including the object indicated by the object identification information received in step S101, image acquisition may be started from any group. Then, the process proceeds to step S509.

  (Step S518) The guide image acquisition unit 203 determines whether there are a plurality of guide images generated in step S509. If there are more than one, the process proceeds to step S520. If not, the process proceeds to step S521.

  (Step S519) The guide image acquisition unit 203 combines a plurality of guide images to acquire one guide image.

  (Step S520) The guide image acquisition unit 203 outputs the guide image acquired in step S519. Then, the process returns to the upper process.

  In the flowchart of FIG. 19, the process ends when the power is turned off or the process ends.

  FIG. 20 is a flowchart showing details of the process of acquiring deformed map information by the map information processing apparatus 1, and shows the process corresponding to step S402 in FIG. Hereinafter, details of the process of acquiring the deformed map information will be described. Here, the case where the classification unit 202 acquires the type information of the deformed map using the peer relationship between objects will be described as an example. In FIG. 20, the same reference numerals as those in FIG. 5 denote the same or corresponding processes.

  (Step S601) The object classification unit 201 classifies objects on the nth deformed map into path objects and geographic objects. For example, the object identification information corresponding to each object on the nth deformed map is separated into the object identification information of the path object and the object identification information of the geographic object using a clue phrase or the like.

  (Step S602) The classification unit 202 detects a peer object from the geographic objects classified in step S601.

  (Step S603) The classification unit 202 configures a minimum spanning tree for objects on the nth deformed map.

  (Step S604) The classification unit 202 groups objects using the minimum spanning tree.

  (Step S605) The classification unit 202 determines whether the object on the nth deformed map satisfies the condition of the route map. If satisfied, the process proceeds to step S606. If not satisfied, the process proceeds to step S607.

  (Step S606) The classification unit 202 determines whether the object on the nth deformed map satisfies the object map condition. If satisfied, the process proceeds to step S314. If not satisfied, the process proceeds to step S309.

  (Step S607) The classification unit 202 determines whether the object on the nth deformed map satisfies the object map condition. If satisfied, the process proceeds to step S313. If not satisfied, the process returns to step S314.

  In the flowchart of FIG. 20, in step S315, the deformed map information in which each deformed map, one or more pieces of object identification information extracted from each deformed map, and the type information acquired for each deformed map are associated with each other. The information is stored in the deformed map information storage unit 101. Instead of this step S315, every time the type information is acquired for the nth deformed map in step S309, step S313, or step S314, the nth deformed map is displayed. The deformed map information in which the one or more object identification information extracted from the nth deformed map and the type information acquired for the nth deformed map are associated with each other in the deformed map information storage unit 101 sequentially. even if There.

  In the flowchart of FIG. 20, the process is terminated by powering off or a process termination interrupt.

  Hereinafter, a specific operation of the map information processing apparatus 2 in the present embodiment will be described. The conceptual diagram of the map information processing apparatus 2 is the same as FIG.

  Here, first, an example of processing in which the map information processing apparatus 2 acquires deformed map information from one or more deformed maps will be described.

  First, similarly to the specific example of the first embodiment, a deformed map used for acquiring deformed map information is prepared by crawling or the like and provided to the deformed map receiving unit 104. The deformed map given here is, for example, a deformed map having object identification information combined on the map as a raster (bitmap) image, and the file name is “kimzmap.jpg”.

  FIG. 21 is a diagram illustrating an example of a deformed map provided to the deformed map receiving unit 104.

  When the deformed map is received, the deformed map receiving unit 104 extracts one or more pieces of object identification information from the deformed map by performing character recognition processing such as OCR. The extracted object identification information and the coordinates on the deformed map where the object identification information is recognized are stored in a storage medium (not shown) in association with the deformed map.

  Next, in order to acquire the type information of the deformed map, the object classifying unit 201 first uses one or more pieces of object identification information acquired by the deformed map receiving unit 104 as object identification information (hereinafter referred to as path object) about the path object. Classification) and object identification information about geographic objects (hereinafter referred to as geographic object identification information).

  For example, the object classification unit 201 prepares one or more character strings (for example, roads, national roads, prefectural roads, roads, roads, lines, railroads, etc.) characteristic of a path in advance in a storage medium or the like as a clue phrase. The object identification information including these clue phrases at the beginning and at the end is classified as path object identification information. Other object identification information is classified into geographic object identification information indicating stations, temples and shrines, universities, facilities, and the like. However, here, the object identification information of prefecture names, city names, mountain names, and river names is excluded from classification. That is, these object identification information is not used here when acquiring the type information of the deformed map. The object identification information to be excluded is, for example, by detecting object identification information including a clue phrase such as “city”, “road”, “fu”, “prefecture”, “mountain”, “river” at the end. To detect. For example, from the deformed map shown in FIG. 21, “Sanjo-dori” and “Higashiooji-dori” are acquired as path object identification information, and “Sanjusangen-do” and “Kiyomizu-dera” are acquired as geographic object identification information. The

  FIG. 22 is a diagram showing the objects on the deformed map shown in FIG. 21 classified into path objects 220 and geographic objects 221 (FIG. 22A), and the classified path objects and geographic objects. It is a figure which shows the object classification management information (FIG.22 (b)) to manage. The object classification management information includes items “object identification information” indicating object identification information and “classification” indicating object classification. When the object is a path object, the value of “classification” is “path object”, and when the object is a geography object, the value of classification is “geography object”.

  Next, the classification unit 202 uses, for example, a database having a plurality of tree-structured categories prepared in advance and storing information related to objects in association with one or more categories. Then, the peer object is detected. Here, a peer object is detected using a database provided by WEB or the like via a wireless network. As such a database, for example, Wikipedia (registered trademark) as described above can be used. Specifically, the classification unit 202 searches the database for “the document including the geographic object identification information” detected above, and determines the category associated with the detected one or more documents as the geographic object identification information. Each time it is temporarily stored in a storage medium (not shown).

  Next, the classification unit 202 determines whether two pieces of geographic object identification information included in the plurality of pieces of geographic object identification information are similar based on the similarity of the category to which each piece of geographic object identification information belongs. Specifically, the number of categories to which each geographical object acquired above is counted, and using the counted number of categories, the Jaccard coefficient indicating the strength of the relationship between the objects shown in Expression 1 in the first embodiment. To get. Then, if the acquired value is equal to or greater than a predetermined threshold value, it is determined that the two objects are peers. If the acquired value is less than the threshold value, it is determined that the two objects are not peers. If there is a similar relationship between a plurality of objects, the objects are regarded as peers if the relationship is closed, but may be a peer between two objects if the relationship is not closed. The determination result of the peer object is stored in association with geographic object identification information in a storage medium (not shown), for example. For example, the geographic object identification information in the peer relationship is associated with the peer relationship identification information indicating the same peer relationship.

  FIG. 23 is a schematic diagram showing the geographic object 221 on the deformed map shown in FIG. In the figure, geographic objects to which the same symbol is attached indicate objects that are in a peer relationship (that is, a geographic object corresponding to geographic object identification information to which identification information of the same peer relationship is associated).

  Next, the classification unit 202 configures a minimum spanning tree using coordinates indicating the position of the object on the deformed map.

  FIG. 24 is a schematic diagram showing a minimum spanning tree composed of objects on the deformed map shown in FIG.

  Further, the classification unit 202 performs grouping of objects on the deformed map using the minimum spanning tree. Specifically, the path is divided from an arbitrary object, and the group is divided when an object having a peer relationship with the object once traced appears. As a result, one group does not include a geographic object with a peer relationship. The object identification information of the objects divided into the same group is stored in a storage medium (not shown) in association with the same group identification information. This process is repeated until all objects on the deformed map are traced. For example, here, it is assumed that group A? Group D is associated with each object identification information as group identification information.

  FIG. 25 is a diagram illustrating a state in which the objects on the deformed map illustrated in FIG. 23 are divided into groups. In the figure, each area surrounded by a dotted line indicates a group to which the object belongs, and is a group indicated by group identification information of group A to group D, respectively.

  Next, the classification unit 202 first counts the total number of geographic objects and path objects (number of object identification information) on the deformed map. For example, the number of objects acquired from the deformed map shown in FIG. 25 is “14”. Next, a group including a path object is detected. Groups A and C are detected as groups. Then, the total number of geographic objects and path objects in each detected group is counted. Then, the total is calculated. The total is “12”. Then, the ratio of the total number of objects in the group including the path object to the total number of objects on the deformed map is calculated. The ratio is 12/14 = 0.86. Then, it is determined whether or not this value is larger than a predetermined threshold value. Here, if the threshold is “0.5”, it is determined that the threshold is larger than the threshold. This indicates that the type of this deformed map is either a route map or a mixed map.

  For this reason, the classification unit 202 counts the number of groups divided by the peer object. For example, the number of group identification information is counted. The count number is “4”. Then, it is determined whether or not the count number is equal to or greater than a threshold value indicating the condition of the object map designated in advance. For example, if the threshold is “5”, it is determined that the threshold is less than the threshold. For this reason, the classification unit 202 determines that the type of the deformed map is a route map, acquires “route map” as type information, and temporarily stores it in a storage medium (not shown) in association with the deformed map.

  If the number of groups is equal to or greater than the threshold value, the classification unit 202 determines that the deformed map type is a mixed map, acquires “mixed map” as type information, and deforms the deformed map. Are temporarily stored in a storage medium (not shown).

  Also, if the ratio of the total number of objects in the group including the path object to the total number of objects on the deformed map is less than the threshold, and the number of groups is equal to or greater than the threshold, the deformed map type is set to the object map. Judgment is made, “object map” is acquired as type information, and it is temporarily stored in a storage medium (not shown) in association with the deformed map.

  If there are a plurality of deformed maps received by the deformed map receiving unit 104, the classifying unit 202 repeats the above processing.

  Here, when the minimum spanning tree acquired from the deformed map given to the deformed map receiving unit 104 is as shown in FIG. 26, the total number of objects is eight and the number of groups is five. Further, since the number of objects in the group including the path is 3, it does not correspond to the route map. Further, since the number of groups is five and satisfies the conditions of the object map, this simplified map is classified as an object map.

  Here, when the minimum spanning tree acquired from the deformed map given to the deformed map receiving unit 104 is as shown in FIG. 27, the total number of objects is 17, the number of groups is 10, Satisfy the conditions of the map. Since the number of objects in the group including the path is 12, the route map condition is also satisfied. For this reason, this schematic map is classified into an object map.

  Then, the classification unit 202 includes a deformed map “kymmapmap.jpg” received by the deformed map receiving unit 104, object identification information acquired for the deformed map, information indicating the position of the object indicated by the object identifying information, The type information acquired for the deformed map is stored in the deformed map information storage unit 101 as deformed map information.

  FIG. 28 is a diagram illustrating the deformed map information acquired by the classification unit 202 for the deformed map “kymzmap.jpg” and accumulated in the deformed map information storage unit 101.

  Next, a specific example of processing for acquiring and outputting a guidance image will be described.

  First, the object identification information receiving unit 102 receives the object identification information “Shijo Station” indicating the departure place, the object identification information indicating the destination, and the type information “route map” from the user. Similarly, the deformed map acquisition unit 103 acquires a deformed map corresponding to this from the deformed map information storage unit 101, and one of the acquired deformed maps is selected by the user and output (for example, to a storage medium such as a memory). (Temporary storage). Here, it is assumed that the output deformed map information includes only one deformed map having the file name “kymmapmap.jpg” shown in FIG.

  The guide image acquisition unit 203 acquires the type information “route map” of the deformed map “kymzmap.jpg” from the deformed map information illustrated in FIG. 28. And since the acquired type information is a route map, a WEB search is performed, a route search from the departure point “Shijo Station” to the destination “Sanjusangen-do” is performed, and the destination is reached from the departure point Passage information including actual measurement object identification information (for example, a building name, a place name, a road name, an intersection name, etc.) and a passing order information indicating the passing order is acquired.

  FIG. 29 shows the measured object identification information and the passing order information of the route that passes from the departure place “Shijo Station” to the destination “Sanjusangen-do”, acquired by the guide image acquisition unit 203 by WEB search. It is a figure which shows the passage information which has.

  Next, the guide image acquisition unit 203 acquires the measured object identification information “Shijo Station” having the first passing order from the passing information shown in FIG. 29, and the object identification information that matches this measured object identification information is It is determined whether or not it is in the “object identification information” of the deformed map information corresponding to the deformed map “kymmapmap.jpg” shown in FIG. Here, since there is a matching “Shijo Station”, an image related to the object indicated by the object identification information is acquired. Here, since the object identification information “Shijo Station” is geographic object identification information that matches one of the object identification information received by the object identification information receiving unit 102, the guide image acquisition unit 203 first performs this object identification. An image around a point in the real space indicated by the information is acquired by WEB search. Here, the image around the point is an image (photograph) of the surroundings of the point indicated by the object identification information “Shijo Station”. For example, all the directions using the object identification information “Shijo Station” as a search key This is an image that can be acquired by a WEB search specifying the acquisition direction. Next, since this object identification information indicates the departure place, the three points (three points) move from this point to the point of the real space indicated by the destination object identification information. The image of the direction indicating the destination direction is acquired. The direction of the destination can be obtained from, for example, the coordinates in the real space of the moved point and the coordinates in the real space of the destination. The acquired images are stored in a storage medium (not shown) or the like in association with information indicating the acquisition order.

  Next, the guide image acquisition unit 203 acquires the measured object identification information “Kawabata Dori” whose passing order information is No. 2 from the passing information shown in FIG. 29, and the object identification information that matches this measured object identification information is obtained. Then, it is determined whether or not it is in the “object identification information” of the deformed map information corresponding to the deformed map “kymmapmap.jpg” shown in FIG. Here, no image is acquired because there is no match.

  Next, the measured object identification information “Nanajo-dori” with the third passing order is acquired, and the object identification information that matches this measured object identification information corresponds to the deformed map “kymmapmap.jpg” shown in FIG. It is determined whether or not it exists in the “object identification information” of the deformed map information. Here, since there is a match, an image related to this point is acquired. Since this object identification information is a path object that does not match any of the object identification information received by the object identification information receiving unit 102, an image of the route closest to the direction indicating the destination direction of this point is acquired. For example, it is an image that can be acquired by a WEB search by specifying the course closest to the direction indicating the destination as the acquisition direction using the object identification information “Shijo Station” as a search key. The direction of the destination can be acquired from, for example, the coordinates of the real space of the point indicated by the search key and the coordinates of the real space of the destination. Further, from this point, an image of the moved point is acquired while moving in three stages (three points) in the course direction.

  Next, the guide image acquisition unit 203 acquires the measured object identification information “Sanjusangen-do” whose passing order is No. 4 from the passing information shown in FIG. 29, and object identification that matches this measured object identification information It is determined whether or not the information is in the “object identification information” of the deformed map information corresponding to the deformed map “kymmapmap.jpg” shown in FIG. Here, since there is a match, an image related to the object indicated by the object identification information is acquired. Here, the object identification information “Sanjusangendo” is geographic object identification information that matches one of the object identification information received by the object identification information receiving unit 102, and indicates the destination. First, the guide image acquisition unit 203 moves from this point in the direction of the point indicated by the destination object identification information in three steps (three points) to the destination while moving toward the destination in three steps. Get an image showing the direction. Next, an image around the spot in the real space indicated by the object identification information “Sanjusangen-do” is acquired in the same manner as described above. The acquired images are stored in a storage medium (not shown) or the like in association with information indicating the acquisition order.

  And the guidance image acquisition part 203 connects the acquired image in the acquisition order, and acquires the guidance image which is a moving image. When obtaining a guide image, here, the difference between whether the images constituting the guide image are related to a path object or a geographical object, Depending on the difference or not, information for controlling image switching may be added to the moving image as follows.

  For the boundary between the image acquired for the path object and the image acquired for the geographic object, a transition effect (for example, a fade-in effect) that clearly indicates that the images are different is set. In addition, between images acquired for geographic objects, in the case of a boundary between objects having a peer relationship, a transition effect such as a so-called “dissolve effect” in which images are switched gently is set to express that the change in the image is small. On the other hand, between images acquired from geographical objects that do not have a peer relationship, an effect such as a so-called wipe effect that switches images quickly is set as a transition effect to indicate that the change in the image is large.

  In addition, instead of adding information for controlling switching of images to a moving image, an image indicating switching of images may be generated and added.

  Further, when acquiring an image related to each path object or geographic object, it is preferable to acquire the image as follows. For example, with respect to an image related to a path object, the acquisition direction is acquired by fixing the direction of travel to the destination and displaying the course. In the case of an image relating to the geographical object of the departure place, an image obtained by gently rotating the left and right by 45 degrees toward the geographical object of the departure place is acquired at the departure place. In the case of a geographic object, it is preferable to acquire an image around the object in a certain range. This fixed range is, for example, a range on a path where there are many photographs provided on the online map, and this range is displayed in the direction of travel. Further, the image is acquired by rotating from the advancing direction to the object direction at the point indicated by the path object to which the most images are given in this range.

  Then, the output unit 204 outputs the acquired guide image together with the deformed map “kymzmap.jpg”.

  FIG. 30 is a diagram illustrating an output example of the guide image by the output unit 204.

  FIG. 31 is a schematic diagram in which images constituting the guide image are arranged along the time axis to be reproduced. In the figure, an image 311 indicates an image acquired for “Shijo Station”, an image 312 indicates an image acquired for “Nanajo Dori”, and an image 313 indicates an image acquired for “Sanjusangen-do”. However, the image shown here schematically shows the state of the photographed image for convenience of explanation, and is different from an actual building or a street.

  When the type of deformed map is a route map, instead of acquiring and displaying the above guidance image, the minimum spanning tree as shown in FIG. 24 is acquired and used as shown below. As described above, the guide image may be acquired and displayed.

  The flow of acquiring images from the departure point to the destination will be described below. First, an image around the departure object is acquired. Next, the path object image is sequentially acquired by tracing the minimum spanning tree in the direction toward the destination. As the path object image, an image in the course direction close to the destination direction at the position indicated by the path object is acquired, and further, an image of a position advanced in three stages (three points) in this direction is also acquired. As for the object at the destination, first, images are sequentially acquired while moving in the direction of the destination from a position three steps (for three points) before the destination. Next, an image around the object at the destination is acquired.

  In addition, the image acquired at each path object or the point indicated by the geographic object may be an image other than those described above, and may be an image that appropriately represents the state of the route.

  In addition, here, a case has been described where there is one deformed map as a target for obtaining the guidance image, but there may be a plurality of target deformed maps. As described above, the guide image is acquired, and the acquired guide image is combined to acquire the final guide image.

  As described above, according to the present embodiment, a guide image corresponding to a deformed map can be automatically acquired, and a guide image corresponding to a deformed map can be displayed.

  Note that machine learning is performed using the attribute value acquisition unit 105, the classification unit 106, and the determination information storage unit 1061 of the first embodiment, instead of the object classification unit 201 and the classification unit 202 of the present embodiment. The type of deformed map may be classified as follows. In the first embodiment, instead of using the attribute value acquisition unit 105, the classification unit 106, and the determination information storage unit 1061, the object classification unit 201 and the classification unit 202 are configured in the same manner as in the present embodiment. It may be used to classify the type of deformed map based on the peer relationship of geographic objects.

  In addition, by applying the second embodiment, the map information processing apparatus 2 acquires the type information of the deformed map from the deformed map received by the deformed map receiving unit 104, and the classification unit 202 and the like. By using the guide image acquisition unit 203, a guide image may be generated. In this case, the departure point and the destination may be designated by the object identification information received from the object identification information receiving unit 102, or may be determined by a rule designated in advance. For example, this rule is indicated by the rule that designates the object near the center of the deformed map as the departure point, the rule that designates two geographical objects that are far apart as the departure point and the destination, or the largest letter. For example, a rule for designating an object as a destination.

  In each of the above embodiments, each process (each function) may be realized by centralized processing by a single device (system), or by distributed processing by a plurality of devices. May be.

  In each of the above embodiments, each component may be configured by dedicated hardware, or a component that can be realized by software may be realized by executing a program. For example, each component can be realized by a program execution unit such as a CPU reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory.

  In addition, the software which implement | achieves the map information processing apparatus in each said embodiment is the following programs. That is, this program stores a deformed map information storage unit that stores deformed map information having a deformed map that is not based on actual measurement results and object identification information that is information for identifying an object displayed on the deformed map. An object identification information receiving unit that receives two or more object identification information, and one deformed map corresponding to the two or more object identification information received by the object identification information receiving unit, or two or more objects A program for causing two or more deformed maps respectively corresponding to identification information to function as at least a deformed map acquiring unit and an output unit for outputting the deformed map acquired by the deformed map acquiring unit.

  In the program, the functions realized by the program do not include functions that can be realized only by hardware. For example, a function that can be realized only by hardware such as a modem or an interface card in an acquisition unit that acquires information or an output unit that outputs information is not included in the function realized by the program.

  Further, the computer that executes this program may be singular or plural. That is, centralized processing may be performed, or distributed processing may be performed.

  FIG. 32 is a schematic diagram showing an example of the appearance of a computer that executes the program and realizes the map information processing apparatus according to the embodiment. The above-described embodiment can be realized by computer hardware and a computer program executed on the computer hardware.

  32, a computer system 900 includes a CD-ROM (Compact Disk Read Only Memory) drive 905, a computer 901 including an FD (Floppy (registered trademark) Disk) drive 906, a keyboard 902, a mouse 903, a monitor 904, and the like. Is provided.

  FIG. 33 is a diagram showing an internal configuration of the computer system 900. 33, in addition to the CD-ROM drive 905 and the FD drive 906, a computer 901 is connected to an MPU (Micro Processing Unit) 911, a ROM 912 for storing a program such as a bootup program, and the MPU 911. A RAM (Random Access Memory) 913 that temporarily stores program instructions and provides a temporary storage space, a hard disk 914 that stores application programs, system programs, and data, and an MPU 911 and a ROM 912 are interconnected. And a bus 915. The computer 901 may include a network card (not shown) that provides connection to the LAN.

  A program that causes the computer system 900 to execute the functions of the map information processing apparatus and the like according to the above-described embodiment is stored in the CD-ROM 921 or the FD 922, inserted into the CD-ROM drive 905 or the FD drive 906, and the hard disk 914. May be forwarded to. Instead, the program may be transmitted to the computer 901 via a network (not shown) and stored in the hard disk 914. The program is loaded into the RAM 913 when executed. The program may be loaded directly from the CD-ROM 921, the FD 922, or the network.

  The program does not necessarily include an operating system (OS) or a third-party program that causes the computer 901 to execute the functions of the map information processing apparatus according to the above embodiment. The program may include only a part of an instruction that calls an appropriate function (module) in a controlled manner and obtains a desired result. How the computer system 900 operates is well known and will not be described in detail.

  The present invention is not limited to the above-described embodiments, and various modifications are possible, and it goes without saying that these are also included in the scope of the present invention.

  As described above, the map information processing apparatus and the like according to the present invention are suitable as an apparatus for outputting a deformed map, and in particular, a deformation information corresponding to two or more object identification information specified by a user or the like such as a navigation apparatus. It is useful as a device that outputs a map.

DESCRIPTION OF SYMBOLS 1, 2 Map information processing apparatus 81, 82 Field 83 Selection button 84 Search button 100 Server apparatus 101 Deformation map information storage part 102 Object identification information reception part 103 Deformation map acquisition part 104 Map reception part 104 Deformation map reception part 105 Attribute value acquisition Unit 106, 202 Classification unit 107 Determination unit 108 Map acquisition unit 108 Map information acquisition unit 109 Scale acquisition unit 110, 204 Output unit 201 Object classification unit 203 Guide image acquisition unit 220 Path object 221 Geographic object 1101 Monitor 1061 Determination information storage unit

Claims (15)

A deformed map information storage unit that stores deformed map information including a deformed map that is a map that is not based on actual measurement results, and object identification information that is information for identifying an object displayed on the deformed map;
An object identification information receiving unit that receives two or more object identification information;
The deformed map acquisition unit that acquires at least one deformed map corresponding to two or more object identification information received by the object identification information receiving unit, or two or more deformed maps respectively corresponding to the two or more object identification information. When,
A map information processing apparatus comprising: an output unit that outputs a deformed map acquired by the deformed map acquiring unit. The deformed map information further includes type information that is information indicating the type of deformed map,
The object identification information receiving unit further receives type information,
The map information processing apparatus according to claim 1, wherein the output unit outputs the deformed map so that the deformed map associated with the type information received by the object identification information receiving unit is ranked higher. A deformed map receiving unit that receives the deformed map and one or more object identification information corresponding to the deformed map;
About the deformed map received by the deformed map receiving unit, an attribute value acquiring unit that acquires one or more pre-specified attribute values;
A classification unit that acquires the type information using the attribute value and accumulates the deformed map information in which the type information is associated with the deformed map and the object identification information in the deformed map information storage unit; The map information processing apparatus according to claim 2. A determination information storage unit that stores determination information obtained by machine learning about the relationship between the type of the deformed map and the attribute value of the deformed map;
The classification unit acquires type information of the deformed map using the attribute value acquired by the attribute value acquiring unit for the deformed map and determination information stored in the determination information storage unit. 3. The map information processing apparatus according to 3. The classification unit acquires type information of the deformed map according to a rule that is a condition including one or more attribute values specified in advance using the attribute value acquired by the attribute value acquiring unit for the deformed map. The map information processing apparatus according to claim 3. A deformed map receiving unit that receives the deformed map and one or more object identification information corresponding to the deformed map;
An object classification unit for classifying objects on the deformed map into path objects that are objects indicating roads and geographic objects that are objects other than roads;
A peer object is detected from the geographical objects classified by the object classification unit, and the geographical object and the path object are grouped according to the positional relationship using the peer object, and the deformed map according to the grouping situation. And a classification unit that accumulates the deformed map information in which the obtained type information is associated with the deformed map and the object identification information in the deformed map information storage unit. The map information processing apparatus described. When the deformed map acquisition unit acquires two or more deformed maps, a determination unit that determines whether the distance between the deformed maps is far;
Map acquisition for acquiring an actual measurement map, which is a map based on an actual measurement result, including points indicated by at least one or more objects arranged in each of the two or more deformed maps when the determination unit determines that it is far And further comprising,
The map information processing apparatus according to claim 1, wherein the output unit further outputs an actual measurement map acquired by the map acquisition unit. The map information processing apparatus according to any one of claims 1 to 6, wherein the output unit combines and outputs the two or more deformed maps when the deformed map acquisition unit acquires two or more deformed maps. The position information on the deformed map corresponding to two or more object identification information corresponding to two or more objects on the deformed map acquired by the deformed map acquiring unit and the position information in the real space are respectively acquired and acquired. The apparatus further comprises a scale acquisition unit that acquires the scale of the deformed map using position information,
The map information processing apparatus according to any one of claims 1 to 8, wherein the output unit outputs the one or more deformed maps acquired by the deformed map acquiring unit in association with the scale. A guide image acquiring unit that acquires a guide image that is an image including an image acquired from the WEB at a point indicated by one or more objects on the deformed map according to a type of the deformed map acquired by the deformed map acquiring unit; In addition,
The map information processing apparatus according to claim 2, wherein the output unit further outputs the guide image. The map information processing apparatus according to claim 10, wherein the output unit displays the guide image superimposed on the deformed map. The deformed map type includes an object map that introduces the geographic object, a route map that introduces a route, or a mixed map that introduces both a geographic object and a route,
The guide image acquisition unit acquires an image relating to a geographic object on the deformed map when the type of the deformed map is an object map, and relates to a path object of the deformed map when the type of the deformed map is a route map. The image according to any one of claims 10 and 11, wherein an image is acquired, and when the type of the deformed map is a mixed map, an image relating to a geographic object on the deformed map is acquired for the closest path object on the deformed map. Map information processing device. The navigation apparatus provided with the map information processing apparatus in any one of Claims 1-12. A deformed map information storage unit for storing deformed map information including a deformed map that is a map that is not based on an actual measurement result, and object identifying information that is information for identifying an object displayed on the deformed map; and object identifying information A map information processing method performed using a reception unit, a deformed map acquisition unit, and an output unit,
The object identification information receiving unit receives object identification information of two or more object identification information; and
The deformed map acquisition unit, one deformed map corresponding to two or more object identification information received in the object identification information receiving step, or two or more deformed maps respectively corresponding to the two or more object identification information, At least a deformed map acquisition step to acquire,
A map information processing method, wherein the output unit includes an output step of outputting the deformed map acquired in the deformed map acquisition step. A computer capable of accessing a deformed map information storage unit storing deformed map information having a deformed map that is a map that is not based on actual measurement results and object identification information that is information for identifying an object displayed on the deformed map. The
An object identification information receiving unit that receives two or more object identification information;
The deformed map acquisition unit that acquires at least one deformed map corresponding to two or more object identification information received by the object identification information receiving unit, or two or more deformed maps respectively corresponding to the two or more object identification information. When,
The program for functioning as an output part which outputs the deformed map which the said deformed map acquisition part acquired.

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