WO2017168601A1 - Procédé et système de recherche d'images similaires - Google Patents

Procédé et système de recherche d'images similaires Download PDF

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WO2017168601A1
WO2017168601A1 PCT/JP2016/060291 JP2016060291W WO2017168601A1 WO 2017168601 A1 WO2017168601 A1 WO 2017168601A1 JP 2016060291 W JP2016060291 W JP 2016060291W WO 2017168601 A1 WO2017168601 A1 WO 2017168601A1
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image
search
similarity
partial
area
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PCT/JP2016/060291
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English (en)
Japanese (ja)
Inventor
廣池　敦
裕樹 渡邉
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株式会社日立製作所
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Priority to PCT/JP2016/060291 priority Critical patent/WO2017168601A1/fr
Priority to JP2018507912A priority patent/JP6445738B2/ja
Publication of WO2017168601A1 publication Critical patent/WO2017168601A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor

Definitions

  • the present invention relates to an information retrieval technique for images.
  • a general search technique is a search technique for attached text information related to image / video content.
  • one or more keywords are inputted as a query, and an image / video associated with text information including the keyword is generally returned as a search result.
  • Patent Literature 1, Patent Literature 2, and the like in similar image retrieval, an image feature value obtained by quantifying the characteristics of an image is extracted in advance from an image to be retrieved, and a database is created. To achieve high-speed search.
  • a region similarity indicating a region similarity is calculated for each combination of a plurality of regions for dividing a search target image and a plurality of regions for dividing a query image, and the region similarity is calculated for each region in a query image.
  • the importance of the region is calculated based on the region similarity corresponding to the region, and for each search target image, based on the region similarity and importance corresponding to the combination of each region in the search target image and each region in the query image.
  • Patent Document 1 when the area of a partial region of a query image is relatively small, a large number of images that are out of search intention are hit.
  • the present invention includes a step of detecting a plurality of partial regions included in a query image, a step of extracting a plurality of feature amounts of the detected partial regions, a plurality of extracted feature amounts and a DB.
  • a plurality of feature amounts of image partial regions stored in advance a step of calculating the similarity of the associated feature amounts, and each portion included in the query image according to the calculated similarity
  • a method comprising weighting according to the area of the region, and calculating a similarity between the query image and the search target image based on a total value of values obtained by weighting the similarity of each partial region. I will provide a.
  • the weight is reduced, and as a result, it is possible to reduce the possibility of hitting an image that is out of the search intention.
  • FIG. 1 is a system configuration diagram of an embodiment of the present invention. It is a detailed view of the system configuration of the embodiment of the present invention. It is explanatory drawing of the attention area detection technique used in the Example of this invention. It is explanatory drawing of the symmetry axis in the attention area detection technique used in the Example of this invention. It is an example of a differential filter. It is explanatory drawing of a luminance gradient intensity distribution feature-value. It is a figure which shows the flow of the attention area detection process in the Example of this invention. It is a figure explaining the detail of an attention area detection process. It is explanatory drawing of the detailed process in an attention area detection process. It is a schematic diagram for showing an attention area detection result.
  • FIG. 10 is a schematic diagram of a screen configuration of an application according to a second embodiment.
  • FIG. 1 shows a system configuration when the method of the present invention is applied to an image search service.
  • Reference numeral 100 in FIG. 1 denotes a computer system for providing a search service.
  • Various functions provided by the search service are provided to a user who uses the terminal computer 120 via the network system 110.
  • FIG. 2 shows the internal configuration of the computer system 100 for providing a search service.
  • the processing flow of the image search system targeted by the present invention will be described with reference to FIG.
  • the process for registering an image will be described.
  • the focused region detection unit 212 detects a set of partial regions to be focused on from the registered image 210. Details of the attention area detection processing will be described later with reference to FIG.
  • An image feature quantity is extracted by the search feature quantity extraction unit 213 for each detected partial area.
  • the extracted image feature amount is stored in the database 220 in a format associated with the partial area.
  • the focused region detection unit 232 extracts a set of partial regions to be focused from the query image 230. Details of the attention area detection process will be described later with reference to FIG. Next, an image feature amount is extracted by the search feature amount extraction unit 213 for each detected partial area.
  • the attention area detection unit 212 and the attention area detection unit 232 are described separately for convenience of explanation, but these may be the same processing unit in the computer. The same applies to the search feature quantity extraction unit 213 and the search feature quantity extraction unit 233.
  • the similarity search unit 234 is applied with the method of the present invention.
  • the similarity search unit 234 forms a result of the similar image search from the set of image feature amounts of the partial areas of the query image 230 and the set of image feature amounts of the partial areas of the registered image 210 stored in the database 220.
  • the search result output unit 235 generates information to be returned to the search request source using the similar image search result and various types of information stored in the database 220, and transmits the information to the search request source as the search result 240. To do.
  • a detection method using local symmetry is used as a detection method of the attention region that does not limit the target. Yes. Details of the detection method will be described later with reference to FIG.
  • FIG. 3 is an explanatory diagram relating to the extraction of image feature amounts for evaluating local symmetry.
  • An arbitrary rectangular partial area 302 in the image 301 is further divided into 3 ⁇ 3 blocks to extract an image feature vector group 303 of each block area.
  • These nine image feature quantity vectors are denoted by f00, f10, f20, f01, f11, f21, f02, f12, and f22.
  • the first subscript is the position in the x direction of each block
  • the second subscript is the position in the y direction.
  • FIG. 4 is a diagram showing an axis of symmetry in the attention area detection technique.
  • T0, T1, T3, and T4 represent matrices for applying the mirror transformation about each axis to the image feature quantity vector. That is, T0 is a matrix for mirror conversion to the left and right, T1 is a matrix for mirror conversion about the axis at the upper right 45 degrees, and T3 is a matrix for mirror conversion up and down. T4 is a matrix for mirror conversion about the axis at the lower right 45 degrees.
  • the symmetry in the rectangular partial area can be evaluated by applying the above transformation matrix to the image feature quantity vector extracted in each block area.
  • f00 and f20 existing symmetrically with respect to the y axis are vectors obtained by mirror-transforming f20 left and right, that is, a vector obtained by multiplying f20 by T0 is f00. The closer it is, the higher the symmetry.
  • the left-right symmetry can be expressed as a vector D0 composed of the three vector square distances as shown in [Equation 1].
  • this detection method it is evaluated that the symmetry is high when the symmetry is increased by the transformation of the feature vector. For example, when f00 and f02 used in the calculation of D0 originally have a small variation with respect to the left and right reflection conversion, it is not considered that the left-right symmetry is large.
  • a correction term that quantitatively expresses such a property it is composed of a square distance between feature quantity vectors between corresponding block areas when mirror conversion is not applied, as shown in [Formula 5]. Define the vector E0.
  • Equation 9 is used as a specific evaluation function.
  • the above evaluation function uses the maximum value after evaluating symmetry in each of the four directions.
  • FIG. 5 is an example of a filter for performing numerical differentiation.
  • the luminance gradient vector is derived by applying a two-dimensional numerical differentiation to the black and white gray image. From the luminance gradient vector (gx, gy) on the pixel position (x, y) obtained by the differential filter, the vector direction ⁇ and the square norm p of the vector can be calculated as in [Equation 10]. .
  • the vector direction ⁇ is distributed in the range of 0 to 360 degrees. By quantizing this to an appropriate level at equal intervals and summing the square norm p within the rectangular area, the intensity distribution in the direction of the luminance gradient vector can be expressed as histogram-like data.
  • FIG. 6 is a schematic diagram expressing the concept of calculating the luminance gradient intensity distribution feature amount.
  • the brightness gradient vector 601 is extracted from the image.
  • histogram-like data 602 is calculated by aggregation processing.
  • the number of quantization levels is 8. Also, the center of the first quantization range is made to coincide with the x axis direction.
  • the reflection transformation matrix T3 for evaluating the symmetry about the axis in the lower right 45 degree direction is as shown in [Equation 14].
  • FIG. 7 is a diagram illustrating a processing flow of the region of interest detection unit.
  • S701 is a process for generating a plurality of images converted to an appropriate scale and aspect from the input image.
  • S702 is a process for converting a plurality of generated images into multiple resolutions.
  • S703 is a process of generating a rectangular partial region that is a candidate for the target partial region by scanning processing on the multi-resolution image and calculating the symmetry of each partial region. Details of S701 to S703 will be described later with reference to FIG.
  • S704 is a process of sorting a large number of partial areas generated by the scanning process based on the evaluation value of symmetry, and narrowing down the partial area candidates to be focused on to a predetermined upper number.
  • S705 is a process for determining convergence.
  • the refinement process when it is determined in S705 that the convergence does not occur, the refinement process newly generates a partial area from the current focused area candidate, and calculates the symmetry of each partial area, whereby the focused area candidate Is a process of adding. Details of S706 will be described later with reference to FIG. Narrowing based on symmetry evaluation is performed again on the candidate region of interest configured in this way (S704). If there is no change in the region of interest in the convergence determination 705, it is determined that the target area has converged, and the process is terminated. It should be noted that the process may be terminated by determining that the process has converged when the number of repetitions of S704 to S706 exceeds a certain number.
  • FIG. 8 illustrates S701 to S703 in FIG.
  • the size of the partial region in the image is about 240 ⁇ 240 pixels.
  • the shape of the target partial area is not necessarily limited to a square, and it is often necessary to extract a horizontally long and vertically long rectangular area.
  • the aspect of the original image is deformed to be vertically long, and the symmetry is evaluated by square lattice block division. If the rectangular area generated by such processing is returned to the coordinate system on the original image, a horizontally long rectangle is obtained. Similarly, when it is necessary to extract a vertically long rectangle, the aspect of the original image is transformed into a horizontally long process.
  • FIG. 9 illustrates S706 of FIG.
  • a rectangular region 910 that has been translated slightly vertically and horizontally, a rectangular region 920 that has been slightly enlarged / reduced, and a rectangular region that has been enlarged / reduced are further translated horizontally and vertically with respect to a candidate of a target partial region.
  • a rectangular area is generated as a candidate for a new target partial area.
  • the number of rectangular areas generated by the parallel movement is eight patterns in the up / down, left / right, and diagonal movements.
  • a maximum of 26 new rectangular areas are generated for a single rectangular area, and the symmetry is evaluated.
  • q is the number of repetitions of the detailing process
  • sx and sy are the step widths in the horizontal and vertical directions when the translation is performed in the scanning process (S703), respectively
  • dx and dy are the qth time, respectively.
  • dz is an enlargement ratio in the q-th refinement process, and the reduction ratio in the case of reduction is 1 / dz.
  • the magnitude of the fluctuation decreases with the number of repetitions of this process. Since the target image is a discrete digital image, if this process is sufficiently repeated, new region candidates will not be generated due to minute fluctuations. If at least new area candidates are no longer generated, S704 to S706 are determined to be converged in S705 and are terminated.
  • FIG. 10 schematically shows partial region detection results in the registered image focus area detection unit 212 and the query image focus area detection unit 232.
  • Various partial areas having different sizes are detected from the image 1010.
  • Reference numeral 1020 denotes a set of the respective partial areas.
  • the partial area set 1020 includes a partial area 1021 having a small area and a partial area 1022 having a large area. These will be described later in the description of Expression 18 in the description of FIG.
  • the area of the entire original image may be added as one of the partial areas. Thereby, it is possible to perform a search in consideration of the similarity of the entire image.
  • the image feature quantity used in the search feature quantity extraction unit 213 of the registered image 210 and the search feature quantity extraction unit 233 of the query image 230 is calculated based on color distribution, luminance gradient vector distribution, and the like. Specific definition examples of image feature amounts are disclosed in Non-Patent Document 1, Non-Patent Document 3, and the like.
  • Information regarding the partial area extracted from the registered image 210 in this way is stored in the database 220.
  • FIG. 11 lists items related to the similarity search process in the present embodiment, among the information stored for each partial area.
  • Item 1101 is information for specifying which image each partial area is included in, and is expressed as an integer image ID.
  • An item 1102 is a coordinate value of each rectangular partial area, and stores the coordinate values of the upper left end point and the lower right end point as four integer values.
  • Item 1103 is the ratio of the area occupied by each partial region in the original image. For example, when the partial area matches the original image, the maximum value is 1.0.
  • An item 1004 indicates an image feature amount expressing shape information, and an item 1005 indicates an image feature amount expressing color distribution information. In this embodiment, these two types of feature values are used, but one type may be used, or three or more types of feature values may be set.
  • information related to the query image is also extracted as shown in FIG.
  • the information about the query image is temporarily stored in the memory, and the application of this embodiment can be operated.
  • it is assumed that a search request with different search conditions occurs a plurality of times using the same query image it is inefficient to analyze the query image every time. Therefore, it is desirable that the information about the query image is cached beyond one search process.
  • the cache is performed on the memory, memory consumption increases, which is not appropriate. In such a case, it is preferable to adopt a format for temporarily storing information on the database for the query image.
  • the partial area is defined as a rectangular area.
  • the shape of the partial area does not have to be rectangular.
  • the requirements of the partial area are that the image feature amount can be extracted and that the area of the area is defined.
  • FIG. 12 shows the flow of processing in the similarity search unit 234.
  • S1201 is a process in which the similarity search unit 234 executes a similarity search for the partial region feature amount database. In this process, a similar search is performed by using the feature amount of each partial area detected from the query image as many as the number of partial areas of the query image.
  • S1202 is a process of associating the partial area of the query image with the partial area of each image based on the set of search results acquired in S1201. Details will be described later with reference to FIG.
  • S1203 is a process of configuring the distance between the query image and each image based on the result of association in the process 1202.
  • S1204 is a process for constructing a similar image search result by sorting the distances constructed in process 1203.
  • S1205 is a process of delivering the result to the search result output unit 235.
  • a similarity search result is performed using the square distance between the feature quantities between the partial area of the query image and the partial area on the database as an index of dissimilarity.
  • the square distance in the feature amount space between the i-th rectangular area of the query image and the j-th partial area stored in the database is as shown in [Equation 16].
  • q_ik is the k-th element of the image feature quantity of the i-th partial area
  • f_jk is the k-th element of the image feature quantity of the j-th partial area on the database
  • M is the dimension of the image feature quantity. Is a number.
  • an approximate neighborhood vector search algorithm based on clustering processing is used to speed up the similarity search.
  • the approximate neighborhood vector search is a method for realizing a high-speed search by narrowing down vectors that are assumed to exist in the neighborhood range of a vector to be queried from vectors on a database.
  • the algorithm used in the present embodiment returns the approximate solution of N vectors in the vicinity of the query as a search result in a form sorted in the order of decreasing square distance. Therefore, the value of the square distance to the partial area on the database not included in the N results is unknown at that time.
  • the similarity between the query image and the image stored on the database is constructed from the result of the similarity search for the partial area.
  • This configuration is realized by associating the partial area of the query image with the partial area of each image on the database, and summing up the similarity for each partial area for each image.
  • Each result 1300 of the similarity search in this embodiment is composed of the calculated square distance, the ID of the partial area, and the ID of the image including the partial area. As for the image ID, after performing a similar search, the value is acquired by referring to the information on the database using the partial region ID.
  • Each row of the table in FIG. 13 is a result of a similar search using each partial region of the query image as a query.
  • the search results in each row are arranged in the order of small square distance, that is, the order of high similarity, regardless of which image contains the partial area on the database.
  • the partial area of the query image is associated with the partial area of each image on the database from the set of similar search results.
  • association methods the system operator or the user to search selects either.
  • search results for different partial areas of the query image may include partial areas on the same database. Therefore, in the first method, there is a possibility that different partial areas of the query image are associated with partial areas on the same database. Details of the first method will be described with reference to FIG.
  • FIG. 14 shows the result of association performed by the first method from the search results shown in FIG. Since the search results include the image IDs 1, 2, 5, 6, and 7, the search results that are most similar to the partial regions of the query image are selected for the five images. .
  • the second method is different from the first method in that partial areas on the same database are not associated with multiple.
  • a set of search results from all partial regions of the query image is configured, and the set is sorted in ascending order of the square distance, and then handled in order from the top. To do.
  • the partial area already adopted for the association is excluded and the process proceeds. In this manner, it is possible to associate the query images with a small square distance, that is, a combination of the query image partial area and the image partial area on the database without priority overlap. Details of the second method will be described with reference to FIG.
  • FIG. 15 shows the result of association performed by the second method from the search results shown in FIG.
  • the partial area whose ID of the third partial area is 1 is the partial area whose ID of the partial area whose similarity is higher is 2 is the first partial area Is associated with the partial area whose image ID is 3 in the lower partial area.
  • the fourth partial area since there is no corresponding partial area, the image with the image ID of 1 is not associated.
  • the value of the square distance of the lowest search result in the search results of each partial area is set for the indefinite partial area.
  • the square distance 0.80 when the first partial area is indefinite, the square distance 0.80, and when the second partial area is indefinite, the square distance 0.60 is set.
  • the square distance 0.50 is set, and when the fourth partial area is indefinite, the square distance 0.70 is set as the square distance when each area is indefinite.
  • a statistical value of the square distance for example, an average and a variance is obtained statistically on a sufficiently large database, and a value based on the statistical value is set.
  • the square distance between each partial area included in the query image and the image on the database can be defined as the square distance of the associated partial area.
  • the distance between the query image and the image on the database is defined as [Equation 17] using the square distance between the partial region of the query image defined in this way and the image on the database.
  • D_i is a combined square distance between the query image and the i-th image in the database
  • L is the number of partial regions included in the query image
  • d_ji is the j-th partial region of the query image and i
  • S_j is the ratio of the area of the jth partial region to the area of the query image
  • P is a parameter that controls the effect of S_j.
  • Z is a normalization term defined as:
  • This normalization term is intended to make it easier for the application to handle the value of D_i, and does not affect the search process itself.
  • the control parameter P in Equation 17 performs the following functions.
  • the square distance that is the dissimilarity is used for comparing the similarity, but the similarity that increases when the similarity is high may be used.
  • a negative square distance such as [Equation 19] is exponentially converted.
  • Equation 19 reaches a maximum value of 1.0 when the square distance d is 0.0, and approaches 0.0 when d increases. Even when such a similarity is used, the method shown in Equation 16 can be applied. That is, the similarity between images is basically calculated as the sum of the similarities between partial areas. If there are many similarities between partial areas, the similarity between images increases. The effect of the weighted addition based on the area of the partial region of the query image is not different from that in the case of Expression 17.
  • the result of the similar image search is configured by sorting by the value of D_i.
  • FIG. 16 is a system configuration diagram of the search service described in this embodiment.
  • This service provides the user with the functions of the search system 1600 through the WebAPI 1602.
  • An appropriate calculation resource such as the number of CPUs, the amount of memory, and the amount of hard disk is allocated to the hardware configuration of the search system 1600 according to the scale of the search target and the frequency of search requests.
  • Various programs running on this system exchange information by inter-process communication.
  • the user issues a search request using the Web browser 1601.
  • the search request is received by the WebAPI 1602 of this service, and the result is returned to the Web browser 1601 as a response of the WebAPI 1602.
  • Processing 231, 232, 233, 234, and 235 related to the search in FIG. 2 is performed on the Web server 1603.
  • the Web server 1603 sends the received query image analysis result to the temporary DB server 1604, and the temporary DB server 1604 stores the contents on the file system.
  • the temporary DB server 1604 a plurality of processes can be operated if redundancy is required for stable operation of the system. When a plurality of temporary DB server processes are operating, the Web server 1603 can select a temporary DB server with the smallest load as a registration destination.
  • a process corresponding to the “similar search for a partial region feature DB” 1201 in FIG. 12 is performed not by the Web server 1603 but by the search server 1605.
  • parallel distributed processing is performed by operating a plurality of search servers 1605.
  • the data registration program 1606 sends the analysis result of the acquired registered image to the search server 1605, and the search server 1605 stores the contents on the file system.
  • the information managed regarding the partial area is the same as that shown in FIG.
  • FIG. 17 is a list of items managed on the database for each image.
  • An item 1701 is an array of partial area IDs included in the image, and is managed as a variable-length integer array.
  • An item 1702 is the size of an image, and the number of vertical and horizontal pixels is stored as two integer values.
  • An item 1703 is the date when the image is registered, and is managed as an integer value.
  • An item 1704 is a keyword for performing a narrowing search. Items 1703 and 1704 are not used in the temporary DB server 1604.
  • An item 1705 is image data, and an item 1706 is a thumbnail image, both of which are used to display a search result screen.
  • not only a simple similar image search but also a similar image search function combined with the search date 1703 and the search target narrowing by the keyword 1704 is provided.
  • This function first narrows down the search target of the partial area by referring to the array 1701 of the partial area ID after narrowing down the target image by evaluating the search condition formula. As a result, an efficient search is possible.
  • FIG. 18 is a schematic diagram of a search screen displayed on the Web browser 1601.
  • FIG. 18 is a screen for setting search conditions.
  • Reference numeral 1811 denotes an area for setting a query image.
  • the user sets a query for similarity search by placing an arbitrary image file existing on his file system in the area 1811 on the screen by a drag-and-drop operation. To do.
  • the search button 1812 When the user clicks the search button 1812, a screen transition occurs and a search result screen 1820 is displayed.
  • the query image specified in 1811 is converted into a thumbnail image and displayed in 1821, and a list of similar search results is displayed in 1822.
  • a row of buttons 1823 is for page-turning, and is for browsing an image having a lower rank, that is, a lower similarity. If an image displayed in 1822 is clicked, a similarity search using the clicked image as a query is executed, and the content of the search result screen 1820 is updated. This makes it possible to perform a similar search using an image registered on the database as a query.
  • the search condition setting screen 1810 is a GUI component for narrowing down the search target image by the registered date.
  • the user can input the registration date range by two numerical values representing the lower limit and the upper limit. If there is no input, narrowing down by registration date is not performed. For example, when only the lower limit is set, the search target is from the input date and after to the latest registered image.
  • Reference numeral 1814 denotes a GUI component for setting a narrow-down condition based on a keyword. The user can input an arbitrary character string into the text field. If there is no input, filtering by keyword is not performed. When both the condition based on the registration date and the condition based on the keyword are set, the logical product (AND) is a narrowing condition.
  • the GUI component 1815 composed of two toggle buttons is for selecting an image feature amount used for similarity search.
  • the user checks the button labeled “SHAPE” if he / she wants to perform a search that emphasizes the similarity of shapes, and if the user wishes to perform a search which emphasizes the similarity of colors, Check the attached button.
  • the slide bar 1816 is a GUI component for designating the value of the parameter P that controls the effect of the area of the partial region shown in Expression 16.
  • the user can freely set the value in the range of 0.0 to 1.0.
  • the user wants to search for an image that partially includes the query image, the user sets a value close to 1.0. If you want to search for an image containing a part that is common to the part included in the query image, set a value close to 0.0.
  • a slide bar 1816 suitable for specifying a continuous amount is used as a GUI component for realizing this function.
  • what can be actually specified is a value quantized to an appropriate level. It is.
  • the Web server 1603 caches search results under the same search condition in order to cope with screen transitions associated with the operation of the page switching button 1823.
  • the search condition changes, it is necessary to generate a new cache, and it is not efficient in terms of system operation to generate a cache following a subtle change in P that has no essential effect.
  • the similarity search process is performed after appropriately quantizing the value of P received by WebAPI.
  • quantization processing since it is premised that quantization processing is performed, there is no problem even if the GUI component is not a slide bar but a method such as selection of a value on a button row corresponding to a quantized value.
  • GUI parts similar to those in the search condition setting screen 1810 1813, 1814, 1815, and 1816 are arranged. With these GUI parts, the user can perform a search by changing only other search conditions without changing the query image. This search is executed when the search button 1825 is clicked, and the contents of the search result screen 1820 are updated.

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Abstract

Selon la technologie classique, lorsque la zone d'une région partielle d'une image requête est relativement petite, un grand nombre d'images qui sont enlevées d'une intention de recherche peuvent être considérées comme des réponses pertinentes. La présente invention concerne un procédé de recherche d'images similaires, comprenant les étapes suivantes: une étape de détection d'une pluralité de régions partielles qui sont incluses dans une image requête; une étape d'extraction d'une pluralité de valeurs de traits caractéristiques des régions partielles détectées; une étape d'association respective de la pluralité extraite de valeurs de traits caractéristiques à des valeurs de traits caractéristiques d'une pluralité de régions partielles d'image qui sont stockées préalablement dans une base de données, et le calcul des similarités respectives des valeurs de traits caractéristiques associés; une étape d'attribution d'une pondération aux similarités calculées respectives en fonction des zones de chacune des régions partielles qui sont incluses dans l'image requête; et une étape de calcul de la similarité entre l'image requête et une image sujet de recherche sur la base de la valeur totale de valeurs dans lesquelles un processus de pondération a été effectué concernant les similarités respectives des régions partielles.
PCT/JP2016/060291 2016-03-30 2016-03-30 Procédé et système de recherche d'images similaires WO2017168601A1 (fr)

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