EP2377055A1 - Recherche d'image mobile et système et procédé d'indexation - Google Patents

Recherche d'image mobile et système et procédé d'indexation

Info

Publication number
EP2377055A1
EP2377055A1 EP09837177A EP09837177A EP2377055A1 EP 2377055 A1 EP2377055 A1 EP 2377055A1 EP 09837177 A EP09837177 A EP 09837177A EP 09837177 A EP09837177 A EP 09837177A EP 2377055 A1 EP2377055 A1 EP 2377055A1
Authority
EP
European Patent Office
Prior art keywords
viewing content
pointing device
image
mobile
content cone
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP09837177A
Other languages
German (de)
English (en)
Other versions
EP2377055A4 (fr
Inventor
Christopher Edward Frank
David Caduff
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Intelligent Spatial Technologies Inc
Original Assignee
Intelligent Spatial Technologies Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US12/645,248 external-priority patent/US8184858B2/en
Priority claimed from US12/645,243 external-priority patent/US8745090B2/en
Priority claimed from US12/645,231 external-priority patent/US8675912B2/en
Application filed by Intelligent Spatial Technologies Inc filed Critical Intelligent Spatial Technologies Inc
Publication of EP2377055A1 publication Critical patent/EP2377055A1/fr
Publication of EP2377055A4 publication Critical patent/EP2377055A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
    • G06F16/587—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using geographical or spatial information, e.g. location
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/50—Information retrieval; Database structures therefor; File system structures therefor of still image data
    • G06F16/58—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Definitions

  • the present invention generally relates to computer-implemented systems and methods for image searching and indexing. More specifically, the present invention relates to computer-implemented systems and methods that are used for image searching and indexing that may be incorporated in whole or in part a mobile device.
  • FLICKR photo-sharing database
  • FLICKR http://www.flickr.com
  • FLICKR is a registered trademark of Yahoo, Inc.
  • the accuracy of the results depends on the text entered, not only by the system user, but by the person assigning descriptions to the photo, e.g., keyword tags attached to the picture.
  • entering the keyword "apple” in FLICKR produces over 100,000 potential returns with pictures that range from fruits to clothing styles to computers. These results would fall short of answering the system user's actual question: "apple” that is fruit.
  • the present invention overcomes these problems of conventional image search systems and provides a system and method for image searching and indexing that provides accurate, timely, and comprehensive results.
  • the present invention includes computer- implemented systems and methods for image searching and image indexing that may be incorporated in a mobile device that is part of a computer-implemented object pointing and identification system.
  • the present invention relates to a computer-implemented mobile image searching and indexing system ("MISIS") client that may be associated with computer-implemented mobile pointing and identification system, such as described in U.S. Patent No. 7,245,923, or co-pending U.S. Patent Application No. 12/645,231, U.S. Application No. 12/645,243, and U.S. Application No. 12/645,248.
  • image searching refers to finding images in a database.
  • image indexing refers to analyzing the image context, annotating the content of images, and relating the image and this information with a reference system that makes it easy to retrieve the information.
  • the MISIS client that is incorporated in mobile device includes a camera, a global positioning system ("GPS") receiver or other positioning determining unit, and a digital compass.
  • the MISIS client also may have local storage associated with it and the MISIS client connects wirelessly to a MISIS server that includes storage or has access to storage. Storage at these locations will permit image search result processing either locally on the mobile device including the MISIS client or remotely on a MISIS server.
  • the MISIS client is contemplated to be expandable to accept other inputs, including infrared for night imaging and sketches. This latter use may be helpful when electro-optical visibility is impaired.
  • the MISIS client wirelessly connects to MISIS system server that provides a computational infrastructure for indexing, storing, updating, and retrieving images.
  • the MISIS system server connects wired or wirelessly to storage that includes a multimedia content section and a geographic information system ("GIS") data section. These are for storing the images and providing contextual information based on which images are indexed, including, but not limited to, information about geographic locations and the environment surrounding these geographic locations.
  • GIS geographic information system
  • the MISIS client is preferably directed to processing in situ images. As such, the MISIS client would be preferably used for still images in geographic space taken from the perspective of a system user located near the surface of the Earth. Therefore, the orientation of the images would be approximately horizontal.
  • mobile device incorporating the MISIS client will use the spatial context, i.e., position and orientation, of the MISIS client to search and index images. This will enable the image search engine to become faster and more effective, and provide fewer false-positive results.
  • the MISIS client also will provide quality filtering that minimizes false-positives and false-negatives.
  • a mobile device that incorporates the MISIS client for image searches will improve the system user's searching ability and the ability to learn about objects in his/her surroundings and focus on potential dangers.
  • Figure 1 shows a representative diagram incorporating the MISIS system of the present invention that includes the MISIS client and MISIS server that connects to the MISIS client.
  • Figures 2A, 2B, and 2C show projections of image ranges into 2-D plane at different pointing directions and viewing angles.
  • Figures 3A, 3B, and 3C show different possibilities for false hits for spatial image searches based on indexed locations.
  • Figure 4 shows an example of infrastructure objects that lie in whole or in part in a viewing content cone from a viewing location and infrastructure objects that lie outside of the viewing content cone.
  • Figures 5A, 5B, 5C, and 5D show a progression of image searching
  • the present invention is directed to computer-implemented systems and methods for image searching and image indexing that may be incorporated in mobile devices that is part of object pointing identification systems. More particularly, the present invention relates to a computer-implemented MISIS client and MISIS server that may be associated with computer-implemented mobile pointing and identification systems. The present invention may be used for the searching and indexing of objects in in situ images in geographic space taken from the perspective of a system user located near the surface of the Earth including horizontal, oblique, and airborne perspectives.
  • mobile device 102 may be a mobile device according to U.S. Patent No.
  • mobile device 102 includes MISIS client 104, camera 106, digital compass 124, local storage (not shown) associated with MISIS client 104, and a GPS receiver (not shown) for carrying out the method of the present invention.
  • Digital compass 124, the local storage, and GPS receiver may not be exclusively dedicated to MISIS client and may carry other tasks for the mobile device and still be within the scope of the present invention.
  • MISIS client 104 connects to MISIS server 108 via a wired or wireless connection.
  • MISIS client 104 connects to MISIS server 108 via a wireless connection, such as the Internet 105.
  • MISIS server 108 includes at least geospatial search engine 110, image search engine 112, and
  • MISIS server has storage unit 115 associated with it that preferably stores at least multimedia content at 116 and GIS data at 118.
  • geospatial search engine 110 is a search engine that is accessible by system users to perform search queries related to a geographic or spatial domain, and through which system users will receive search results generated by the search engine in response to search queries.
  • the geographic search engine is also capable of displaying other information about the spatial domain, and through which system users will receive such as attributes that link to the spatial domain.
  • Image search engine 112 is a specialized search engine for finding pictures or images on the web or in a dedicated database. To search for images using the image search engine, system users will input search terms, such as keywords, image files/links, or click on an image, and the image search engine will return images "similar" to the query.
  • search terms such as keywords, image files/links, or click on an image
  • the similarity measures used for search criteria include, but are not limited, meta tags, color distribution in images, or region/shape attributes. It is understood that other similarity measures may be used and still be within the scope of the present invention.
  • SNTGGA unit 114 is for supporting Location Based Services ("LBS") processes.
  • LBS are information and entertainment services accessible by mobile devices through a mobile network. LBS also make use of the geographical position of the mobile device. LBS can deliver location-aware content to system users on the basis of the geographic position of the mobile device and the wireless infrastructure.
  • Multimedia content section 116 is for storing tagged and indexed multimedia captured by the MISIS client. Multimedia content section 116 stores, for example, images, and audio or video files.
  • GIS data section 118 is used to provide context for indexing and storing multimedia by image search engine 112.
  • GIS data section 118 includes geographic data such as geographic points, geographic lines, geographic regions, or 3-D structures that are used to describe objects in a spatial domain.
  • External Data Sources/Content Providers/Search Engine block 120 preferably connects to MISIS server 108 wirelessly via the Internet 105 and provides access to other multimedia that is not locally stored by MISIS server 108 at storage unit 115.
  • multimedia from External Data Sources/Content Providers/Search Engine block 120 may be indexed by MISIS server 108 or multimedia from MISIS client 104 can be linked to External Data Sources/Content Providers/Search Engine block 120 and sent to MISIS server 108.
  • GPS satellites 122 provide latitude and longitude information to mobile device 102 for determining the position of the mobile device, which includes camera 106.
  • Digital compass is 114, which preferably is incorporated as part of mobile device 102, will define the pointing direction of the camera 106 for purposes of the present invention.
  • the pointing direction also will define the centerline of a viewing content cone that emanates from camera 106.
  • this viewing content cone is used for purposes of searching and indexing an image for identifying images relating to objects of interest, such as building 126, with a high degree of accuracy and reliability.
  • FIG. 1 when a system user takes a picture or a movie of a building or landmark, such as shown at 126, with a mobile device that includes MISIS client 104, that picture is sent to MISIS server 108 where the image is tagged and indexed by image search engine 112. The tagged and indexed image is then stored in multimedia content section 116 for later retrieval as a result of a system user query.
  • FIGs 2A, 2B, and 2C projections of image ranges into a two-dimensional ("2-D") plane from different pointing directions and with different viewing angles are shown generally at 200, 220, and 230, respectively.
  • location 202 is a point from which the projection emanates.
  • a mobile device incorporating the MISIS client of the present invention would be located at location 202.
  • the pointing direction of the mobile device located at 202 is shown in phantom at 201.
  • Given viewing angle 204, rays 206 and 208 define viewing field 210 for the mobile device in a 2-D plane.
  • Viewing angle 223 may be the same or different from viewing angle 204 and Figure 2A. The viewing angle will depend on the mobile device. Given viewing angle 223, rays 224 and 226 define viewing field 228 for the mobile device in a 2-D plane.
  • the mobile device at 232 is shown with pointing direction 231 shown in phantom.
  • the viewing angle for a new mobile device at 232 is shown at 233.
  • rays 234 and 236 define viewing field 238.
  • the viewing field of the mobile device at 232 is much less than the viewing field of the mobile device at 202 in Figure 2A and the mobile device at 222 in Figure 2B.
  • the mobile devices at 202, 222, and 232 in Figures 2A, 2B, and 2C, respectively, will include camera 106 ( Figure 1). Therefore, each viewing field will originate from the camera location on the mobile device with a center of the field being the pointing direction in which a picture is taken.
  • the projected range of the viewing field in the 2-D plane will be based on the horizontal viewing angle derived from the camera's focal length at the time the picture was taken.
  • the viewing angle may vary considerably based on the type of camera objective, wide-angle, typically between 60° and 100°, or telephoto, typically between 10° to 15°. The viewing angle may be altered using these parameters if in fact the camera has a zooming capability.
  • Figures 3A, 3B, and 3 C generally at 300, 320, and 330, respectively, show different possibilities for false hits for spatial image searches based on indexed locations.
  • the actual captured image is shown at 302. This image would be stored in a system database.
  • GPS-enabled camera 304 is shown at location 306.
  • the viewing angle of camera 304 is shown at 303.
  • the viewing field for camera 304 will be defined by rays 308 and 310 formed by viewing angle 303 considering the focal length of the lens of camera 304.
  • location 306 of image 302 there is a high probability of false-positive hits because only the camera is located within the image search area. More particularly, none of the image content would be located in the viewing field.
  • GPS-enabled camera 304 is shown at location 322. Again, the viewing angle of camera 304 is shown at 303.
  • the viewing field of camera 304 will be defined by rays 308 and 310 formed by viewing angle 303 considering the focal length of the lens of camera 304. Given the location of camera 304 at 322, there will be fewer false-positive hits than in Figure 3A, but only nearby content will be included in the results while more likely content in area 326 of image 302 would be excluded because only a small portion of the image falls within the viewing field.
  • GPS-enabled camera 304 is shown at location 332, which is outside image area 302.
  • the viewing field of camera 304 will be defined by rays 308 and 310 formed by viewing angle 303 considering the focal length of the lens of camera 304. Given the location of camera 304 at 332 outside image area 302, there will be a high probability of false-negatives hits because of this camera location. Further, a large majority of the potential objects would be missed in area 336 of the image. [0042] As has been shown with respect to Figures 3A, 3B, and 3C, the content of an in situ image is constrained by the pointing direction of the camera at the time of image recordation and the viewing angle in a conventional indexing model.
  • the present invention integrates the GPS-enabled capabilities of cameras along with the viewing direction and viewing angle for each image so that a much more accurate assessment of the content of the in situ image is carried out.
  • spatial parameters that are used for the more accurate assessment of content of in situ images include location information captured by a GPS receiver, pointing direction by a digital compass, and the camera angle by the object's focal length at the time of recording of an image. The combination of these parameters will generate a content viewing field (viewing content cone). This viewing content cone will provide a much more accurate reference system for indexing potential infrastructure content captured in an image.
  • the viewing content cone depth may be defined by additional parameters, which include, but are not limited to, the horizon or visual impairments, such as fog or smoke. Further, viewing field depth may be a default value set by the System Administrator. Although the present invention preferably focuses on the depth of the content viewing field in a 2-D plane (a viewing content cone), it is understood other shapes, including three-dimensional ("3-D") shapes, are within the scope of the present invention. For example, 3-D conical or pyramid shapes are within the scope of the present invention.
  • the viewing content cone according to the present invention provides a quality filter for searching an image.
  • FIG. 4 As a quality filter, the viewing content cone will consider static objects of the image that are not included in the viewing content cone as not being part of the image and, therefore, cannot become false-positives when searching. This will be described in more detail referring to Figure 4.
  • a camera at location 402 has viewing angle 404. Given viewing angle 404, rays 406 and 408 formed by the focal length of the lens of the camera 402 will define viewing content cone 425. Therefore, according to the present invention, viewing content cone 425 acts as a quality filter.
  • MISIS indexing is based on the content of the object-based GIS datasets stored in storage 115 at 118. These datasets contain the footprints of individual geospatial instances or landmarks as they are used in spatial cognition and communication. These datasets may also contain 3-D representations of the objects in the viewing content cone.
  • the present invention links a viewing content cone with the GIS datasets for the purpose of MISIS spatial content-based indexing and searching. Further, the use of a spatial index according to the present invention will allow for fast identification and recognition of objects that are visible from the system user's specific point of view. This point of view is a major consideration because it is the point from which indexing takes place. It is understood that the system user's point of view would mean at least the location of the system user's camera that is part of the MISIS client.
  • the linking process according to the present invention will be based on predetermined indexing trees.
  • These indexing trees may be used for indexing objects contained in images of the environment.
  • indexing objects means identifying objects contained in an image, annotating the image accordingly, and linking the image to the indexing tree in a database.
  • indexing trees will be described, it is understood that more or less than these four indexing trees may be used and still be within the scope of the present invention.
  • BSP A Binary Space Partitioning ("BSP") Tree organizes objects within a space according to a cutting plane.
  • the cutting plane is used to categorize objects in the space as either being in "front” or in "back” of the plane. For example, consider a cube and a cutting plane that divides the cube into equally sized partitions. If the view direction is based on the cutting plane, objects encompassed by the partitions can now be described as being in front of the cutting plane or in back of the cutting plane. This process is iteratively applied to each partition, until the partitions conform to some criteria, such as containing only a single object.
  • Octree The space around the origin point is divided up into eight octants. Each octant is marked occupied or free according to whether there is any object occupying that location in the environment to be represented. Each occupied octant is then divided again into eight subspaces and the process continues recursively until sufficient resolution has been achieved. More particularly, the Octree method iteratively partitions space in regular cubes until the spatial units are fully contained in the leaves of the tree. Again consider the cube containing a set of objects as a starting point, the cube will be subdivided into eight uniform cubes. This process is iteratively applied until each object is mapped into the tree.
  • R-Tree The space is split into hierarchically nested, and possibly overlapping, minimum bounding rectangles.
  • Each node of an R-tree has a variable number of entries (up to some pre-defined maximum).
  • Each entry within a non-leaf node stores two pieces of data: a way of identifying a child node, and the bounding box of all entries within this child node.
  • a 2-D plane that contains a set of objects. This plane is subdivided into minimal bounding rectangles with each containing a set of minimum bounding rectangles. This process is iteratively applied on each minimum bounding rectangle until each minimum bounding rectangle contains a set of individual objects that is less than a predetermined maximum number.
  • KD-Tree The KD-tree is a binary tree in which every node is a k- dimensional point. Every non-leaf node generates a splitting hyperplane that divides the space into two subspaces. Points left to the hyperplane represent the left sub-tree of that node and the points right to the hyperplane represent the right sub-tree.
  • the hyperplane direction is chosen in the following way: every node split to sub-trees is associated with one of the k-dimensions, such that the hyperplane is perpendicular to that dimension vector.
  • indexing trees are used in combination with thematic data from External Data Sources/Content Providers/Search Engine block 120 and multimedia content section 116 linked to spatial objects to identify contents in an image and annotate the image accordingly. Therefore, according to the present invention, this combination supports efficient and fast retrieval of subsets of objects for query processing. Further, as the indexing trees provide information about the topological setup of the image, reliable indexing of the image takes place within the viewing content cone.
  • the MISIS index is generated by intersecting the viewing content cone with a spatial data set that includes the area in which the image is taken.
  • the data set can be either 2-D or 3-D.
  • the intersection that is based on a spatial indexing tree mechanism identifies the objects that are candidates for indexing.
  • the image is updated with information about image content, i.e., thematic data about spatial objects in the image, and spatial content, i.e., position and orientation, and the spatial indexing tree is updated with information about available images.
  • the indexing and updating workflow includes four process steps.
  • the system user captures some multimedia, such as a picture, with their mobile pointing device that includes a MISIS client.
  • the media (the picture) is sent to the MISIS server where it is tagged, annotated, an indexed based on the spatial context from location and orientation information captured by the MISIS client at the time of picture creation.
  • the annotated and indexed media is stored in a multimedia database.
  • a second system user uses a MISIS client to query, find, and retrieve media based on the stored context information that tagged and annotated the media.
  • the MISIS indexing system is updated to include all additions and changes.
  • Spatial context such as location and orientation, are used to index the media, which will mean that when a system user is taking a picture on their vacation with their cell phone, these pictures will be tagged automatically.
  • the tags will describe, for example, what the picture is of, such as the "Parthenon, Athens, Greece” or "8 Elm Street, Orono, Maine 04473.”
  • two incremental settings of the MISIS index are distinguished according to Boolean process that will be described referring to Figures 5A, 5B, 5C, and 5D.
  • FIG. 5A shown generally at 500, a scene is shown having two images taken from two index points, point Pl at 502 and point P2 at 504.
  • viewing content cone 506 is generated.
  • viewing content cone 506 captures objects 510, 512, and 514.
  • viewing content cone 520 is generated according to the pointing direction, viewing angle, and focal length of the lens of the camera at point P2.
  • Viewing content cone 520 captures objects 512, 514, 522, and 524.
  • searching window 542 is shown with respect to the scene that includes objects 510, 512, 514, 522, 524, 530, and 532.
  • search window 542 includes no objects found in viewing content cone 506 that relates to point Pl at 502.
  • object 522 is found in viewing content cone 520 that relates to point P2 at 504.
  • searching window 552 is shown with respect to the scene that includes objects 510, 512, 514, 522, 524, 530, and 532.
  • search window 552 in a search for images, it is seen that search window 552 includes objects 510, 512, and 514 found in viewing content cone 506 that relates to point Pl at 502. It is also seen that search window 552 includes objects 512 and 514 found in viewing content cone 520 that relates to point P2 at 504. Accordingly, objects 512 and 514 are found in both viewing content cones while only object 510 is found in viewing content cone 506.
  • searching window 562 is shown with respect to the scene that includes objects 510, 512, 514, 522, 524, 530, and 532.
  • search window 562 in a search for images, it is seen that search window 562 includes no objects found in viewing content cone 506 that relates to point Pl at 502. It is also seen that search window 562 includes object 524 found in viewing content cone 520 that relates to point P2 at 504.
  • the results of the processing according to Figures 5 A, 5B, 5C, and 5D are a list of objects that will be used to tag and annotate the image.
  • the MISIS Boolean process described with respect to Figures 5A, 5B, 5C, and 5D determine whether or not an image contains a particular infrastructure object or conversely whether an infrastructure object is shown only within a particular image. This process may be carried out using an index over 2-tuples, which can be stored in and retrieved from a relational database that is part of MISIS server 108 or other storage location including on the MISIS client.
  • the information that is retrieved may be, for example, the image that shows the South side of 11 Oak Street and the north side of 8 Elm Street.
  • the retrieval of information using an index over 2-tuples can be very rapid with retrieval times preferably within seconds.
  • An example of an index over 2-tuples includes, but is not limited to, the following: ⁇ object ID, image ID> ⁇ image ID, object ID>.
  • the results of the first two queries include sets of identifiers that can be logically combined with results of a number of these types of queries through, preferably, SQL query statements.
  • the two sets of identifiers preferably are a set of image identifiers and a set of object identifiers. These results can serve as input for visual browsing or for more time-consuming image processing analysis.
  • MISIS relevance is attached to each object to indicate how well each image represents that object.
  • relevance value ranges between "0" (not represented) and "1" (completely represented). For example, a MISIS relevance value could be "0.5.” This would mean that the image represents the object in a manner that is 50% of what could be a complete representation of the object.
  • the relevance value is generated based on the criteria that includes, but is not limited to, nearness, centrality, and overlap. These three criteria will now be described; however it is understood that more or less than these criteria may be used and still be within the scope of the present invention.
  • Nearness refers to the position of the object to the camera location within the viewing content cone.
  • this relevance measure is a higher value the closer the object is located to the camera. However, if an object is too close to the camera lens, it will be blurred and the relevance measure for very close objects will be lower.
  • Centrality refers to the object's location with respect to the camera's viewing angle. Preferably, this second relevance measure is higher for objects that are just closer to the centerline of the viewing content cone and lower the closer to the rays that define the limits of the viewing content cone. The centrality measure is based on the assumption that objects of major interest tend to be located at the center of the picture, while objects that are of lesser interest are typically located near the periphery.
  • Overlap Overlap refers to the capture of the object within a viewing content cone. Preferably, this third relevance measure is higher for objects captured completely and lower for partial pictures of objects. The overlap or obstruction of objects in an image will be correlated with information from the spatial indexing information from GIS data section 118 to provide metric details for the measurement of the overlap criteria.
  • the MISIS relevance index is associated with each spatial object in a viewing content cone.
  • the image index is stored for each object in GIS data section 118 or in MISIS server 108 at 114, but may also be stored on mobile device 102.
  • the MISIS image index that is stored preferably includes 6-tuples.
  • An example of a 6- tuple image index that is stored in MISIS server 108 at 114 and 118, includes, but is not limited to, the following: ⁇ object ID, image ID, relevance measure, camera location, camera angle, date/time>.
  • MISIS relevance index enables a system user to input the following types of queries:
  • MISIS relevance index will permit more advanced visual analyses of images. For example, using MISIS relevance index a system user could create a visual walk around an object by sorting the images in a clockwise or counterclockwise sequence. The system user could also create a visual walk towards an object starting from a specific location. The system user could also geolocate and track moving objects with respect to infrastructure objects.
  • inventions or portions thereof of the system and method of the present invention may be implemented in computer hardware, firmware, and/or computer programs executing on programmable computers or servers that each includes a processor and a storage medium readable by the processor (including volatile and non-volatile memory and/or storage elements).
  • Any computer program may be implemented in a high-level procedural or object-oriented programming language to communicate within and outside of computer-based systems.
  • Any computer program may be stored on an article of manufacture, such as a storage medium (e.g., CD-ROM, hard disk, or magnetic diskette) or device (e.g., computer peripheral), that is readable by a general or special purpose programmable computer for configuring and operating the computer when the storage medium or device is read by the computer to perform the functions of the embodiments.
  • a storage medium e.g., CD-ROM, hard disk, or magnetic diskette
  • device e.g., computer peripheral
  • the embodiments, or portions thereof may also be implemented as a machine-readable storage medium, configured with a computer program, where, upon execution, instructions in the computer program cause a machine to operate to perform the functions of the embodiments described above.
  • the embodiments, or portions thereof, of the system and method of the present invention described above may be used in a variety of applications. Although the embodiments, or portions thereof, are not limited in this respect, the embodiments, or portions thereof, may be implemented with memory devices in microcontrollers, general purpose microprocessors, digital signal processors (DSPs), reduced instruction-set computing (RISC), and complex instruction-set computing (CISC), among other electronic components. Moreover, the embodiments, or portions thereof, described above may also be implemented using integrated circuit blocks referred to as main memory, cache memory, or other types of memory that store electronic instructions to be executed by a microprocessor or store data that may be used in arithmetic operations.
  • DSPs digital signal processors
  • RISC reduced instruction-set computing
  • CISC complex instruction-set computing

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Library & Information Science (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Processing Or Creating Images (AREA)

Abstract

L'invention porte sur un système et un procédé mis en œuvre par ordinateur pour une recherche d'image et une indexation d'image, qui peuvent être incorporés dans un dispositif mobile faisant partie d'un système d'identification d'objet. L'invention porte sur un système et un procédé mis en œuvre par ordinateur, apparentés à un client MISIS et un serveur MISIS, qui peuvent être associés à un système de pointage et d'identification mobile pour la recherche et l'indexation d'objets dans des images in situ dans un espace géographique pris à partir de la perspective d'un utilisateur de système situé à proximité de la surface de la terre comprenant des perspectives horizontale, oblique et aérienne.
EP09837177.6A 2008-12-30 2009-12-30 Recherche d'image mobile et système et procédé d'indexation Withdrawn EP2377055A4 (fr)

Applications Claiming Priority (5)

Application Number Priority Date Filing Date Title
US14154708P 2008-12-30 2008-12-30
US12/645,248 US8184858B2 (en) 2008-12-22 2009-12-22 System and method for linking real-world objects and object representations by pointing
US12/645,243 US8745090B2 (en) 2008-12-22 2009-12-22 System and method for exploring 3D scenes by pointing at a reference object
US12/645,231 US8675912B2 (en) 2008-12-22 2009-12-22 System and method for initiating actions and providing feedback by pointing at object of interest
PCT/US2009/069860 WO2010078455A1 (fr) 2008-12-30 2009-12-30 Recherche d'image mobile et système et procédé d'indexation

Publications (2)

Publication Number Publication Date
EP2377055A1 true EP2377055A1 (fr) 2011-10-19
EP2377055A4 EP2377055A4 (fr) 2013-04-17

Family

ID=44502295

Family Applications (1)

Application Number Title Priority Date Filing Date
EP09837177.6A Withdrawn EP2377055A4 (fr) 2008-12-30 2009-12-30 Recherche d'image mobile et système et procédé d'indexation

Country Status (4)

Country Link
EP (1) EP2377055A4 (fr)
JP (2) JP5608680B2 (fr)
CA (1) CA2748178A1 (fr)
WO (1) WO2010078455A1 (fr)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8060112B2 (en) 2003-11-20 2011-11-15 Intellient Spatial Technologies, Inc. Mobile device and geographic information system background and summary of the related art
US7245923B2 (en) 2003-11-20 2007-07-17 Intelligent Spatial Technologies Mobile device and geographic information system background and summary of the related art
US7418341B2 (en) 2005-09-12 2008-08-26 Intelligent Spatial Technologies System and method for the selection of a unique geographic feature
US8538676B2 (en) 2006-06-30 2013-09-17 IPointer, Inc. Mobile geographic information system and method
WO2010075456A1 (fr) 2008-12-22 2010-07-01 Intelligent Spatial Technologies, Inc. Système et procédé pour déclencher des actions et communiquer un retour par pointage au niveau d'un objet intéressant
WO2010075455A1 (fr) 2008-12-22 2010-07-01 Intelligent Spatial Technologies, Inc. Système et procédé pour explorer des scènes tridimensionnelles par pointage au niveau d'un objet de référence
US8483519B2 (en) 2008-12-22 2013-07-09 Ipointer Inc. Mobile image search and indexing system and method
JP5436574B2 (ja) 2008-12-22 2014-03-05 インテリジェント スペイシャル テクノロジーズ，インク． ポインティングによって現実世界のオブジェクトとオブジェクト表現とをリンクさせるシステム及び方法
KR102256057B1 (ko) * 2014-03-17 2021-05-25 에스케이플래닛 주식회사 객체의 자세 기반 검색 결과 제공 장치, 그 방법 및 컴퓨터 프로그램이 기록된 기록매체
US11514083B2 (en) * 2016-12-22 2022-11-29 Nippon Telegraph And Telephone Corporation Data processing system and data processing method

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6930715B1 (en) * 2000-07-21 2005-08-16 The Research Foundation Of The State University Of New York Method, system and program product for augmenting an image of a scene with information about the scene
US20070162942A1 (en) * 2006-01-09 2007-07-12 Kimmo Hamynen Displaying network objects in mobile devices based on geolocation
US20080069404A1 (en) * 2006-09-15 2008-03-20 Samsung Electronics Co., Ltd. Method, system, and medium for indexing image object
EP2154481A1 (fr) * 2007-05-31 2010-02-17 Panasonic Corporation Dispositif de capture d'image, serveur de fourniture d'informations supplémentaires, et système de filtrage d'informations supplémentaires

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6070167A (en) * 1997-09-29 2000-05-30 Sharp Laboratories Of America, Inc. Hierarchical method and system for object-based audiovisual descriptive tagging of images for information retrieval, editing, and manipulation
US20040021780A1 (en) * 2002-07-31 2004-02-05 Intel Corporation Method and apparatus for automatic photograph annotation with contents of a camera's field of view
US7245923B2 (en) * 2003-11-20 2007-07-17 Intelligent Spatial Technologies Mobile device and geographic information system background and summary of the related art
US7495582B2 (en) * 2005-03-08 2009-02-24 Northrop Grumman Corporation Geographic information storage, transmission and display system
US20070055441A1 (en) * 2005-08-12 2007-03-08 Facet Technology Corp. System for associating pre-recorded images with routing information in a navigation system
US8243081B2 (en) * 2006-08-22 2012-08-14 International Business Machines Corporation Methods and systems for partitioning a spatial index
JP2008158583A (ja) * 2006-12-20 2008-07-10 Hitachi Software Eng Co Ltd 画像関連情報表示システム

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6930715B1 (en) * 2000-07-21 2005-08-16 The Research Foundation Of The State University Of New York Method, system and program product for augmenting an image of a scene with information about the scene
US20070162942A1 (en) * 2006-01-09 2007-07-12 Kimmo Hamynen Displaying network objects in mobile devices based on geolocation
US20080069404A1 (en) * 2006-09-15 2008-03-20 Samsung Electronics Co., Ltd. Method, system, and medium for indexing image object
EP2154481A1 (fr) * 2007-05-31 2010-02-17 Panasonic Corporation Dispositif de capture d'image, serveur de fourniture d'informations supplémentaires, et système de filtrage d'informations supplémentaires

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of WO2010078455A1 *

Also Published As

Publication number Publication date
JP2014241165A (ja) 2014-12-25
WO2010078455A1 (fr) 2010-07-08
EP2377055A4 (fr) 2013-04-17
JP5608680B2 (ja) 2014-10-15
JP2012514261A (ja) 2012-06-21
CA2748178A1 (fr) 2010-07-08

Similar Documents

Publication Publication Date Title
US8873857B2 (en) Mobile image search and indexing system and method
JP5608680B2 (ja) モバイルの画像検索及びインデキシングのシステム及び方法
Luo et al. Geotagging in multimedia and computer vision—a survey
US8996305B2 (en) System and method for discovering photograph hotspots
Zheng et al. Research and applications on georeferenced multimedia: a survey
US9020529B2 (en) Computer based location identification using images
US7860317B2 (en) Generating search results based on duplicate image detection
US9689703B2 (en) Presenting hierarchies of map data at different zoom levels
US20110184949A1 (en) Recommending places to visit
EP2695134A2 (fr) Détermination d'évènement à partir de photos
US20080027985A1 (en) Generating spatial multimedia indices for multimedia corpuses
US20110218984A1 (en) Method and system for searching for information pertaining target objects
US9208171B1 (en) Geographically locating and posing images in a large-scale image repository and processing framework
Choi et al. Multimodal location estimation of consumer media: Dealing with sparse training data
Joshi et al. Using geotags to derive rich tag-clouds for image annotation
Fazal et al. Efficiency of Web Crawling for Geotagged Image Retrieval.
Ennis et al. High-level geospatial information discovery and fusion for geocoded multimedia
Christodoulakis et al. Picture Context Capturing for Mobile Databases.
Spyrou et al. An overview of Flickr challenges and research opportunities
Aydin et al. An architecture for surroundings discovery by linking 3D models and LOD cloud
GENTILE Using Flickr geotags to find similar tourism destinations
Shao et al. Towards Accurate Georeferenced Video Search With Camera Field of View Modeling
US20230044871A1 (en) Search Results With Result-Relevant Highlighting
Deeksha et al. A spatial clustering approach for efficient landmark discovery using geo-tagged photos
Ennis et al. A System for Real-Time High-Level Geo-Information Extraction and Fusion for Geocoded Photos

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20110715

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO SE SI SK SM TR

DAX Request for extension of the european patent (deleted)
A4 Supplementary search report drawn up and despatched

Effective date: 20130315

RIC1 Information provided on ipc code assigned before grant

Ipc: G06F 17/30 20060101AFI20130311BHEP

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20170701