WO2017107865A1 - Système d'extraction d'image, serveur, base de données et procédé associé - Google Patents

Système d'extraction d'image, serveur, base de données et procédé associé Download PDF

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WO2017107865A1
WO2017107865A1 PCT/CN2016/110341 CN2016110341W WO2017107865A1 WO 2017107865 A1 WO2017107865 A1 WO 2017107865A1 CN 2016110341 W CN2016110341 W CN 2016110341W WO 2017107865 A1 WO2017107865 A1 WO 2017107865A1
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image
feature point
retrieval
matching
feature
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PCT/CN2016/110341
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English (en)
Chinese (zh)
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陈卓
李薪宇
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成都理想境界科技有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • 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
    • G06F16/583—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content

Definitions

  • the present invention relates to the field of image retrieval technologies, and in particular, to an image retrieval database, an image retrieval database generation method, an image retrieval server, an image retrieval system, and an image retrieval result troubleshooting method.
  • the feature extraction is first performed on the image and the feature content is described, and the target image is characterized by the feature point set.
  • the retrieval result of the target image is determined based on the matching relationship between the pair of feature points.
  • the search result may be erroneous (ie, the target image and the search result are two images that are irrelevant).
  • the search results may also be correct. For the above reasons, there is a need for a method of troubleshooting search results to ensure that the image retrieval results are as accurate as possible.
  • the object of the present invention is to provide an image retrieval database, an image retrieval database generation method, an image retrieval server, an image retrieval system, and an image retrieval result troubleshooting method, and solve the problem of too many retrieval results caused by the limitation of the feature description image algorithm. Can effectively improve the accuracy of image retrieval.
  • the present invention provides an image retrieval database generating method, comprising: performing feature extraction on a sample image or a preprocessed sample image to obtain a feature point set corresponding to each sample image;
  • the position information, the scale, the direction and the feature description information of each feature point in the feature point set corresponding to the sample image in the image region are recorded in the search data corresponding to the sample image in the image retrieval database; according to each sample image
  • Positional information of each feature point in the image region is set in the corresponding feature point, and feature points in the feature point set corresponding to the sample image are spatially sorted
  • the sample image is constructed according to the spatial sorting result of each sample image a Delaunay triangulation network, and record the triangular sequence data of the constructed Delaunay triangulation network in the retrieval data corresponding to the sample image in the image retrieval database, the triangle sequence data including three points corresponding to each triangle in the Delaunay triangular network And the serial number of the three sides.
  • the spatial ordering refers to performing median ordering according to position information of each feature point in the image region in the feature point set, and specifically includes: a sorting axis determining step, and the feature points in the feature point set have the x-axis and the y-axis The axis of the largest/minimum diameter is used as the sorting axis; an updating step of calculating the median value of the two feature points constituting the maximum/minimum diameter on the sorting axis, and changing the original feature point set to be spatially located to the left of the median value
  • the feature point is located to the left of the median point in the data set, and the feature point that is spatially located to the right of the median is located to the right of the median point in the data set; the loop step, the set of points to the left point and the right side
  • the point set consisting of dots performs the sorting axis determining step and the updating step until the number of feature points on the median side is less than two.
  • the present invention also provides an image retrieval database.
  • the image retrieval database stores retrieval data of a plurality of sample images, and the retrieval data of each sample image includes feature point set data of the sample image and Delaunay constructed by the feature point set.
  • Triangular sequence data of a triangular network the feature point set data includes position information, scale, direction, and feature description information of each feature point in the feature point set in the image region;
  • the triangle sequence data includes each triangle in the Delaunay triangular network The corresponding three points and the serial number of the three sides.
  • the present invention also provides an image retrieval result troubleshooting method.
  • the image retrieval is an image retrieval based on feature extraction.
  • the extracted feature data includes each feature point in the image region.
  • image retrieval result refers to one or more sets of search result image data in the image retrieval database that meets the matching algorithm with the target image, and the image retrieval result troubleshooting method is used for each Search results images for troubleshooting, including:
  • the posture relationship between the target image and each of the retrieval result images is calculated by using the matching feature point pair scale and direction information, and the matching feature point pair set includes the target image matching feature point set and the retrieval result image matching.
  • the coordinates of the target image matching feature point set are converted into coordinates in the image coordinate system of the search result, and the feature points of the target image matching feature point set are spatially sorted according to the coordinates converted by the coordinate system, according to Sorting results construct a Delaunay triangular network corresponding to the target image matching feature point set;
  • the Delaunay triangulation network corresponding to the search result image is obtained from the image retrieval database, and the unmatched feature point subset is deleted in the Delaunay triangle network corresponding to the search result image, and the Delaunay corresponding to the search result image matching feature point set is obtained.
  • the spatial ordering refers to a median ordering, and specifically includes: a sorting axis determining step of using an axis having a maximum/minimum diameter on the x-axis and the y-axis as a sorting axis; and an updating step, in the On the sorting axis, calculate the median value of the two feature points constituting the maximum/minimum diameter, and change the original feature point set so that the feature point spatially located to the left of the median value is located to the left of the median point in the data set, and makes the space
  • the feature point on the right side of the median is located to the right of the median point in the data set;
  • the looping step repeats the sorting axis determining step and the point set on the point set formed by the left point and the right point The update step until the number of feature points on the median side is less than two.
  • the method further includes: pre-storing retrieval data of a plurality of sample images in an image retrieval database, wherein the retrieval data of each sample image includes feature point set data of the sample image and a triangle of a Delaunay triangulation network constructed by the feature point set Sequence data; the feature point set data includes position information, scale, direction, and feature description information of each feature point in the feature point set in the image region; the triangle sequence data includes three triangles corresponding to each triangle in the Delaunay triangle network The number of points and three sides.
  • the present invention also provides an image retrieval result troubleshooting method.
  • the image retrieval is an image retrieval based on feature extraction.
  • the extracted feature data includes each feature point in the image region.
  • image retrieval result refers to one or more sets of search result image data in the image retrieval database that meets the matching algorithm with the target image, and the image retrieval result troubleshooting method is used for each Search results images for troubleshooting, including:
  • the posture relationship between the target image and each of the retrieval result images is calculated by using the matching feature point pair scale and direction information, and the matching feature point pair set includes the target image matching feature point set and the retrieval result image matching.
  • the feature points of the target image matching feature point set in the coordinate system are divided into subsets to obtain multiple target image matching.
  • each target image matching feature point subset is spatially sorted according to the coordinates converted by the coordinate system, and a Delaunay triangular network is constructed according to the sorting result for each target image matching feature point subset;
  • the Delaunay triangulation network corresponding to the search result image is obtained from the image retrieval database, and the unmatched feature point subset is deleted in the Delaunay triangular network corresponding to the search result image, and the matched feature point pair set is obtained.
  • Each of the search result images matches the Delaunay triangulation network corresponding to the subset of feature points;
  • the image retrieval result is determined to be correct; otherwise, the image retrieval result is determined. error.
  • the spatial ordering refers to a median sorting
  • the sorting axis determining step uses an axis having a maximum/minimum diameter on the x-axis and the y-axis of the feature point set as a sorting axis; and an updating step on the sorting axis Calculating the median value of the two feature points constituting the maximum/minimum diameter, changing the original feature point set such that the feature point spatially located to the left of the median value is located to the left of the median point in the data set, and is spatially located The feature point on the right side of the value is located to the right of the median point in the data set; the looping step repeats the sorting axis determining step and the updating step on the point set formed by the left point and the point set formed by the right point, Until the number of feature points on the median side is less than two.
  • the method further includes: pre-storing retrieval data of a plurality of sample images in an image retrieval database, wherein the retrieval data of each sample image includes feature point set data of the sample image and a triangle of a Delaunay triangulation network constructed by the feature point set Sequence data; the feature point set data includes position information, scale, direction, and feature description information of each feature point in the feature point set in the image region; the triangle sequence data includes three corresponding to each triangle in the Delaunay triangle network The number of the point and the three sides.
  • the present invention also provides an image retrieval server comprising an image retrieval database and an image matching module, wherein the image retrieval database is the image retrieval database proposed above; and the image matching module is configured to receive images from the image retrieval Retrieving the request by the client, and matching the target image data included in the retrieval request in the image retrieval database according to the set matching algorithm, to obtain one or more sets of retrieval result image data satisfying the matching algorithm with the target image;
  • the image retrieval server further includes a debugging module, and the debugging module includes:
  • the attitude calculation and coordinate system conversion unit is configured to calculate the attitude relationship between the target image and each of the retrieval result images according to the image retrieval result, and use the matching feature point pair scale and direction information, and according to the calculated posture relationship, Converting coordinates of the target image matching feature point set into coordinates in the search result image coordinate system, the matching feature point pair set including the target image matching feature point set and the retrieval result image matching feature point set;
  • the triangular network building unit is configured to spatially sort the feature points in the target image matching feature point set according to the coordinates converted by the coordinate system, and construct a Delaunay triangular network corresponding to the target image matching feature point set according to the sorting result;
  • a judging unit configured to obtain a Delaunay triangulation network corresponding to the search result image from the image retrieval database; and delete the unmatched feature point subset in the Delaunay triangulation network corresponding to the search result image, to obtain a search result image matching feature point
  • the Delaunay triangle network corresponding to the set, and matching the search result image with feature points The Delaunay triangular network corresponding to the set is compared with the Delaunay triangular network corresponding to the target image matching feature point set; if the two triangular network comparison results are consistent, the image retrieval result is determined to be correct; otherwise, the image retrieval result is determined to be incorrect.
  • the present invention further provides an image retrieval server, including an image retrieval database, an image matching module, and a retrieval result information returning module, wherein the image retrieval database is the image retrieval database proposed by the present invention; the image matching module, And receiving the search request from the image retrieval client, and matching the target image data included in the retrieval request in the image retrieval database according to the set matching algorithm, to obtain one or more sets of retrieval results satisfying the matching algorithm with the target image.
  • Image data; the retrieval result information returning module is configured to return the retrieval result information to the image retrieval client;
  • the image retrieval server further includes a debugging module, and the debugging module includes:
  • the attitude calculation and coordinate system conversion unit is configured to calculate the attitude relationship between the target image and each of the retrieval result images by using the matching feature point pair scale and direction information according to the image retrieval result, and according to the calculated posture relationship,
  • the coordinates of the target image matching feature point set are converted into coordinates in the search result image coordinate system, and the matching feature point pair set includes a target image matching feature point set and a retrieval result image matching feature point set;
  • the subset dividing unit is configured to perform subset partitioning on the feature points of the target image matching feature point set after the coordinate system is converted according to the position of the image point of the search result corresponding to the target image matching the feature point in the search result image. Obtaining a plurality of target image matching feature point subsets;
  • the triangular network building unit is configured to spatially sort the feature points of each target image matching feature point subset according to the coordinates converted by the coordinate system, and construct a Delaunay triangular network for each target image matching feature point subset according to the sorting result;
  • a judging unit configured to obtain a Delaunay triangulation network corresponding to the search result image from the image retrieval database, and delete the unmatched feature point subset in the Delaunay triangulation network corresponding to the search result image, to obtain a matching feature point pair
  • the search result image matches the Delaunay triangulation network corresponding to the subset of feature points, and compares the two Delaunay triangular networks corresponding to each subset pair. If the subset of the preset ratio exceeds the preset ratio, the two triangle network comparison results are consistent. , the image retrieval result is determined to be correct; otherwise, the image retrieval result is determined to be incorrect.
  • the present invention also provides an image retrieval system including an image retrieval client and an image retrieval server, wherein:
  • the image retrieval server is the image retrieval server proposed by the present invention.
  • the image retrieval client includes an image acquisition module, a feature extraction module, a retrieval request sending module, and a retrieval result receiving module, wherein:
  • the image acquisition module is configured to acquire a target image
  • the feature extraction module is configured to perform feature extraction on the target image, and the extracted feature data includes location information, scale, direction, and feature description information of each feature point in the image region;
  • the search request sending module is configured to send the feature data extracted by the feature extraction module to an image retrieval server for image retrieval;
  • the search result receiving module is configured to receive the search result information returned from the image retrieval server.
  • the invention uses the Delaunay triangulation network to characterize the internal relationship of the image feature point set, and uses the unique property of the Delaunay triangulation network to debug (correct) the search result, and the algorithm is correct (the bottom line of the constraint condition is satisfied), but the human recognition Knowing that the search results that are determined to be erroneous are eliminated, can effectively improve the accuracy of the search results.
  • FIG. 1 is a schematic flow chart of a method for generating an image retrieval database according to an embodiment of the present invention
  • FIG. 2 is a schematic diagram of a feature point set in an embodiment of the present invention.
  • FIG. 3 is a first schematic flowchart of a method for troubleshooting an image retrieval result according to an embodiment of the present invention
  • FIG. 4 is a second schematic flowchart of a method for troubleshooting an image retrieval result according to an embodiment of the present invention
  • FIG. 5 is a schematic diagram of locations of corresponding matching feature points in a search result image and a target image according to an embodiment of the present invention
  • FIG. 6 is a schematic diagram of a first structure of an image retrieval server according to an embodiment of the present invention.
  • FIG. 7 is a schematic diagram showing a second structure of an image retrieval server according to an embodiment of the present invention.
  • FIG. 8 is a third schematic flowchart diagram of a method for troubleshooting an image retrieval result according to an embodiment of the present invention.
  • FIG. 9 is a fourth schematic flowchart of a method for troubleshooting an image retrieval result according to an embodiment of the present invention.
  • FIG. 10 is a third schematic structural diagram of an image retrieval server according to an embodiment of the present invention.
  • FIG. 11 is a fourth schematic structural diagram of an image retrieval server according to an embodiment of the present invention.
  • the invention adopts the Delaunay triangulation network to characterize the internal relationship of the image feature point set, and uses the unique property of the Delaunay triangulation network to debug (correct) the retrieval result, and the algorithm is correct (the bottom line of the constraint condition is satisfied), but human cognition
  • the search results that were determined to be erroneous are rejected.
  • the Delaunay triangulation network is a network formed by Delaunay triangulation of point sets. To meet the definition of Delaunay triangulation, two important criteria must be met:
  • the minimum angle of the triangle formed by the Delaunay triangulation is the largest.
  • the Delaunay triangle network is the "closest to the regularized" triangular network. Specifically, it means that the diagonal lines of the convex quadrilaterals are formed by two adjacent triangles, and after the mutual exchange, the minimum angles of the six inner corners are no longer increased.
  • the Delaunay Triangulation Network has the following features:
  • Shell with convex polygons The outermost boundary of the triangular network forms a convex polygonal outer shell.
  • the method for troubleshooting the retrieval result of the present invention it is necessary to construct a Delaunay triangular network separately for the target image matching feature point set and the retrieval result image matching feature point set. Since the triangular network comparison is needed, when the Delaunay triangular network is established, the coordinates of the target image matching feature point and the search result image matching feature point need to be transformed into the same coordinate system. Then, the target image matching feature point set and the retrieval result image matching feature point are respectively respectively in the converted coordinate system The Delaunay triangulation is performed on the set to obtain the Delaunay triangulation network corresponding to the target image and the Delaunay triangulation network corresponding to the search result image. Finally, the correctness of the search results is judged by comparing the above two Delaunay triangulation networks.
  • the retrieval result image refers to a sample image that satisfies the matching algorithm.
  • the Delaunay triangulation network corresponding to the target image and the retrieval result image is generated online.
  • the Delaunay triangulation network corresponding to each sample image may be generated offline during the construction of the image retrieval database to improve the efficiency of image retrieval.
  • the following is a first embodiment of the present invention by taking the Delaunay triangulation network corresponding to the sample image offline as an example with reference to FIG. 1 to FIG. 7 .
  • FIG. 1 is a schematic flowchart of a method for generating an image retrieval database according to an embodiment of the present invention. This method is used to construct a dedicated image retrieval database with Delaunay triangular network data corresponding to the sample image.
  • the image retrieval database generating method includes the following steps S101 to S104.
  • feature extraction is performed on the sample image or the pre-processed sample image to obtain a feature point set corresponding to each sample image.
  • feature extraction method a feature-based extraction method based on scale invariance, such as ORB, SIFT, SURF, etc., may be employed.
  • the preprocessed sample image refers to a sample image preprocessed by one or more methods of uniform size processing, redundant area culling, Gaussian blur processing, and affine transformation, and the preprocessing is to improve retrieval precision.
  • the position information, the scale, the direction and the feature description information of each feature point in the feature point set corresponding to each sample image in the image region are recorded in the search data of the corresponding sample image in the image retrieval database.
  • the order in which feature point data is recorded into the image retrieval database is arbitrary.
  • the characterization information can be an 8-byte content description.
  • the feature point position information can be represented by two-dimensional coordinates.
  • S103 spatially sorting the feature points in the feature point set corresponding to each sample image according to the position information of each feature point in the image region. Feature points that are closer in spatial position are still closer together after sorting.
  • S104 constructing a Delaunay triangular network corresponding to the sample image according to the spatial ordering result of each sample image obtained in S103 (the triangular network is unique), that is, constructing the same point set, no matter which point is started A consistent result is obtained; at the same time, the same subset of the point set is deleted, and the resulting triangular network is also consistent).
  • the triangular sequence data of the Delaunay triangulation network corresponding to each sample image constructed is recorded in the retrieval data corresponding to the sample image in the image retrieval database.
  • the triangular sequence data includes the sequence numbers of three points and three sides corresponding to each triangle in the Delaunay triangular network.
  • the spatial ordering in step S103 may be a median ordering.
  • the median ordering refers to the image area according to the feature point
  • the location information within is sorted by median.
  • the axis having the feature points in the feature points on the x-axis and the y-axis has the largest/minimum diameter as the sorting axis. Calculating a median value of two feature points constituting the maximum/minimum diameter on the sorting axis, and changing a set of original feature points such that a feature point spatially located to the left of the median value is located at a median point in the data set Side, and feature points that are spatially to the right of the median are located to the right of the median point in the data set.
  • the x-axis diameter refers to the absolute value of the difference between the maximum value and the minimum value of the x-coordinates of each feature point in the feature point set; the y-axis diameter refers to the difference between the maximum value and the minimum value of the y-coordinate of each feature point in the feature point set.
  • the absolute value refers to the absolute value of the difference between the maximum value and the minimum value of the y-coordinate of each feature point in the feature point set.
  • the set of 7 points consists of a x-axis diameter of 14 and a y-axis diameter of 7.5.
  • the median sorting uses the larger of the x and y-axis circumference diameters as the sorting axis.
  • the x-axis is used as the sorting axis, and the median is 0.
  • Three points (-7.5, 2.5), (-2, 2), (-4, -1.5) are placed to the left of the median point, and the other four points are placed to the right of the median point.
  • the left point set and the right point set are recursively processed, that is, the left and right side point sets are re-searched for the larger diameter axis in the xy axis, and the median values of the two feature points constituting the diameter are calculated, and the original feature point set is changed. So that the feature points that are spatially to the left of the median are located to the left of the median point in the data set, and the feature points that are spatially to the right of the median are located to the right of the median point in the data set.
  • a large number of sample images may be processed on the server side to generate a corresponding image retrieval database, and new sample image data may be added to the existing image separately or in groups in the added mode. retrieve the database.
  • the sample image can be preprocessed in various ways, including uniform size processing, redundant area culling, Gaussian blur processing, affine transformation and so on.
  • a dedicated image retrieval database used in the embodiment of the present invention can be generated.
  • the image retrieval database is stored on the image retrieval server side.
  • the image retrieval database stores retrieval data of a plurality of sample images.
  • the retrieval data of each sample image includes feature point set data of the sample image and triangular sequence data of a Delaunay triangular network constructed from the feature point set.
  • the feature point set data includes position information, scale, direction, and feature description information of each feature point in the feature point set in the image region.
  • the triangular sequence data includes the sequence numbers of three points and three sides corresponding to each triangle in the Delaunay triangular network.
  • the image retrieval database referred to in the embodiment of the present invention in conjunction with the embodiments of Figs. 3 to 7 is the dedicated image retrieval database described in this embodiment.
  • FIG. 3 is a schematic diagram of a first flow of a method for troubleshooting an image retrieval result according to an embodiment of the present invention.
  • the image retrieval is an image retrieval based on feature extraction.
  • the extracted feature data includes position information, scale, direction, and feature description information of each feature point in the image region.
  • the image retrieval result refers to one or more sets of retrieval result image data in the image retrieval database that satisfy the matching algorithm with the target image.
  • the image retrieval result debugging method respectively performs error correction for each of the retrieval result images, and includes the following steps S201 to S205.
  • the matched feature point pair set includes a target image matching feature point set and a retrieval result image matching feature point set.
  • a target image matching feature point and a search result image matching feature point constitute a matching feature point pair.
  • the feature point spatial sorting manner is consistent with the sample image feature point spatial sorting manner when the image retrieval database is generated. For example, when generating an image retrieval database, the feature points of the sample image are sorted by median ordering and the largest diameter among the x and y axis diameters is the sorting axis. In this step, when the feature points in the target image matching feature point set are spatially sorted, it is also required to be performed in the same manner.
  • the Delaunay triangulation network corresponding to the search result image is obtained from the image retrieval database, and the feature point subsets on the unmatched are deleted in the Delaunay triangulation network corresponding to the search result image, and the search result image matching feature point set is obtained.
  • Corresponding Delaunay triangle network Corresponding Delaunay triangle network.
  • the Delaunay triangular network corresponding to the target image matching feature point set and the Delaunay triangular network corresponding to the search result image matching feature point set are compared. If the results of the two triangular network alignments are consistent (the so-called results are consistent, that is, the corresponding point pairs in the point pair set are in the same position in the two Delaunay triangular networks), it is determined that the image retrieval result is correct; otherwise, the image retrieval result is determined to be incorrect.
  • the image matching algorithm is not limited in the embodiment of the present invention, and the image retrieval based on the feature extraction may be performed by using the method of the embodiment of the present invention.
  • step S201 the calculated attitude relationship between the target image and the search result image can be described by a vector of length 6, which is affine[6].
  • step S202 the coordinates of the feature point set of the target image are converted into coordinates in the image coordinate system of the search result according to affine [6], and the conversion expression is as follows:
  • step S203 the point set of (Xr, Yr) is spatially ordered.
  • the target image may be subject to distortion or the like.
  • the process method shown in Figure 3 is used to troubleshoot, the error will be too large.
  • the present invention proposes an improvement.
  • FIG. 4 is a second schematic flowchart of a method for troubleshooting an image retrieval result according to an embodiment of the present invention. This troubleshooting method is improved on the embodiment illustrated in FIG.
  • the image retrieval result troubleshooting method includes the following steps S301 to S306.
  • the matched feature point pair set includes a target image matching feature point set and a retrieval result image matching feature point set.
  • a target image matching feature point and a search result image matching feature point constitute a matching feature point pair.
  • step S302 the coordinates of the target image matching feature point set are converted into coordinates in the search result image coordinate system according to the calculated posture relationship.
  • the conversion method is the same as the conversion method in step S201.
  • the subset of the feature points in the target image matching feature point after the coordinate system conversion is divided into subsets, and more The target images match a subset of feature points.
  • the block is divided into 3*3 blocks to 7*7 blocks, and the feature point subset set in 9 to 49 blocks is subjected to subsequent step processing in units of subsets (ie, the processes in steps S304 to S306 are all in subsets).
  • the error of the calculation result is too large due to the different postures of the feature points.
  • the left side is the search result image
  • the right side is the target image.
  • the two matching feature point pairs include A A', B B', C C', D D', E E', F F'.
  • the search result image feature point A B C D E F corresponding to the target image matching feature point A'B'C'D'E'F' is located in the search result image.
  • the location is divided into subsets.
  • the matching feature points A B C D corresponding to the four points of A'B'C'D' are located in the same region fast in the search result image, and the matching feature points E F corresponding to the two points of E'F' are The search result image is located in the same area fast.
  • the four points of A'B'C'D' are divided into the same target image matching feature point subset in the target image matching feature point set, and the E'F' two points are divided into another in the target image matching feature point set.
  • a target image matches a subset of feature points.
  • the A B C D four points are divided into the same search result image matching feature point subset, and the E F is divided into another search result image matching feature point subset.
  • a target image matching feature point subset corresponds to a search result image matching feature point subset.
  • the corresponding target image matching feature point subset and the retrieval result image matching feature point subset are referred to as a subset pair (ie, matching feature point pair subset).
  • a target image matching feature point subset composed of four points A'B'C'D' and a search result image matching feature point subset formed by A B C D four points are referred to as a subset pair.
  • the reason is as follows: the position of the feature point of the search result image corresponding to the target image matching feature point in the search result image is selected, and the feature point of the coordinate image converted target image matching feature point set is subjected to a subset.
  • the division is because the image retrieval is based on the sample images stored in the database. The sample image is a complete image, and the target image may not be a complete image during the shooting process (ie, only a part of the whole image is taken). If the target image is used as a subset, the probability of error is higher. Big.
  • the feature points in each of the target image matching feature point subsets are spatially sorted according to the coordinates converted by the coordinate system. According to the sorting result, a Delaunay triangulation network is constructed for each target image matching feature point subset.
  • the feature point spatial sorting manner is consistent with the sample image feature point spatial sorting manner when the image retrieval database is generated.
  • a Delaunay triangular network corresponding to the search result image is obtained from the image retrieval database, and the feature point subsets on the unmatched are deleted in the Delaunay triangular network, and the matching feature points of the search result image in the matching feature point pair set are obtained.
  • the Delaunay triangle network corresponding to the episode is obtained from the image retrieval database, and the feature point subsets on the unmatched are deleted in the Delaunay triangular network, and the matching feature points of the search result image in the matching feature point pair set are obtained.
  • the influence of the distorted image on the retrieval result can be effectively reduced, and the accuracy of the retrieval result can be further improved.
  • the embodiment of FIG. 4 does not limit the image matching algorithm, as long as the image retrieval based on feature extraction can be performed by using the method of the embodiment of the present invention.
  • FIG. 6 is a schematic diagram of a first structure of an image retrieval server according to an embodiment of the present invention.
  • the image retrieval server of the present embodiment includes an image retrieval database 10 and an image matching module 11.
  • the image retrieval database 10 is a dedicated image retrieval database described in the foregoing embodiment.
  • the image matching module 11 is configured to receive a retrieval request from the image retrieval client, and match the target image data included in the retrieval request in the image retrieval database according to the set matching algorithm to obtain one or more groups and targets. The image satisfies the search result image data of the matching algorithm.
  • the image retrieval server also includes a debug module 12.
  • the troubleshooting module 12 includes posture calculation and coordinate system rotation The unit 121, the triangular network construction unit 122, and the determination unit 123.
  • the attitude calculation and coordinate system conversion unit 121 is configured to calculate the attitude relationship between the target image and each of the retrieval result images by using the scale and direction information of the pair of matching feature points according to the image retrieval result, and according to the calculated posture relationship. Converting the coordinates of the target image matching feature point set to the coordinates in the search result image coordinate system.
  • the matched feature point pair set includes a target image matching feature point set and a retrieval result image matching feature point set.
  • a target image matching feature point and a search result image matching feature point constitute a matching feature point pair.
  • the triangular network construction unit 122 is configured to spatially sort the feature points in the target image matching feature point set according to the coordinate converted by the coordinate system (the sorting manner is consistent with the sorting manner used in the image retrieval database generation), and construct the target image according to the sorting result. Match the Delaunay triangle network corresponding to the feature point set.
  • the determining unit 123 is configured to obtain a Delaunay triangulation network corresponding to the search result image from the image retrieval database 10, and delete the unmatched feature point subset in the Delaunay triangulation network corresponding to the search result image to obtain a search result image matching.
  • the Delaunay triangular network corresponding to the feature point set compares the Delaunay triangular network corresponding to the search result image matching feature point set with the Delaunay triangular network corresponding to the target image matching feature point set constructed by the triangular network construction unit 122. If the alignment results of the two triangular networks are consistent, it is determined that the image retrieval result is correct; otherwise, the image retrieval result is determined to be incorrect.
  • FIG. 7 is a schematic diagram of a second structure of an image retrieval server according to an embodiment of the present invention.
  • the image retrieval server structure differs from that of FIG. 6 in that the debug module 12 is different in structure.
  • a subset division unit 124 is added to the debug module 12, and the functions of the triangle network construction unit 125 and the determination unit 126 are also different from those of the corresponding unit in FIG. 6, as follows:
  • the posture calculation and coordinate system conversion unit 121 in FIG. 7 is completely consistent with the functions of the posture calculation and the coordinate system conversion unit in FIG. 6, and is used for calculating the target image by using the scale and direction information of the matching feature point pair set according to the image retrieval result.
  • the attitude relationship between each of the search result images is converted, and the coordinates of the target image matching feature point set are converted into coordinates in the search result image coordinate system according to the calculated posture relationship.
  • the subset dividing unit 124 is configured to perform a subset of the feature points in the target image matching feature point set after the coordinate system is converted according to the position of the image point of the search result corresponding to the target image matching the feature point in the search result image. Dividing, obtaining a plurality of target image matching feature point subsets. A detailed description of the subset partitioning is provided in the section describing the FIG.
  • the triangular network construction unit 125 is configured to spatially sort the feature points of each target image matching feature point subset according to the coordinates converted by the coordinate system (the sorting manner is consistent with the sorting manner used in the image retrieval database generation), according to the sorting result
  • Each target image matches a subset of feature points to construct a Delaunay triangulation network.
  • the determining unit 126 is configured to obtain a Delaunay triangle corresponding to the image of the search result from the image retrieval database
  • the network deletes the feature point subsets on the unmatched ones in the Delaunay triangle network corresponding to the search result image, and obtains a Delaunay triangle network corresponding to each of the search result image matching feature point subsets in the matched feature point pair set, and each child
  • the pair pairs are compared to the two Delaunay triangle networks. If the subset of the preset ratio exceeds the alignment result of the two triangular networks, the image retrieval result is determined to be correct; otherwise, the image retrieval result is determined to be incorrect.
  • the image retrieval database mentioned in the embodiments introduced in conjunction with FIGS. 8 to 11 is a conventional image retrieval database in which Delaunay triangular network data of sample images is not stored in advance, and only sample images need to be stored.
  • the feature point set data can be.
  • the feature point set data of each sample image includes position information, scale, direction, and feature description information of each feature point in the feature point set in the image region.
  • FIG. 8 is a third schematic flowchart of a method for troubleshooting image retrieval results according to an embodiment of the present invention.
  • the image retrieval is also an image retrieval based on feature extraction.
  • the extracted feature data includes position information, scale, direction, and feature description information of each feature point in the image region.
  • the image retrieval result refers to one or more sets of retrieval result image data in the image retrieval database that satisfy the matching algorithm with the target image.
  • the image retrieval result debugging method shown in FIG. 8 performs error correction for each of the search result images, respectively, and includes the following steps S401 to S404.
  • the matched feature point pair set includes a target image matching feature point set and a retrieval result image matching feature point set.
  • a target image matching feature point and a search result image matching feature point constitute a matching feature point pair.
  • the coordinates of the target image and the search result image matching feature point set are converted into the same coordinate system according to the calculated posture relationship.
  • the coordinates of the target image matching feature point set may be converted into the search result image coordinate system, or the coordinates of the search result image matching feature point set may be converted into the target image coordinate system.
  • the coordinate system conversion method refers to the description of step S201 in the foregoing embodiment.
  • Delaunay triangulation is performed on the target image matching feature point set and the search result image matching feature point set respectively in the converted coordinate system, and the Delaunay triangular network corresponding to the target image and the Delaunay triangular network corresponding to the search result image are obtained.
  • the Delaunay triangulation is specifically as follows: matching the feature points in the feature points of the target image and the feature points in the image feature points of the search result, spatially sorting according to the coordinates converted by the coordinate system, and constructing corresponding Delaunay triangles according to the sorting results.
  • the spatial ordering can be a median ordering.
  • the specific method of median sorting refers to the description of the median ordering in step S103 in the previous embodiment.
  • FIG. 9 is a fourth schematic flowchart of a method for troubleshooting an image retrieval result according to an embodiment of the present invention. This troubleshooting method is improved on the embodiment illustrated in FIG.
  • the method flow illustrated in FIG. 8 is to perform a Delaunay triangulation on the entire matching feature point set of the target image and the search result image, respectively.
  • the method flow illustrated in FIG. 9 is to first divide a subset of matching feature points of the target image and the search result image, and then perform Delaunay triangulation on each subset separately, and finally use the corresponding pair of Delaunay triangles. The network is compared.
  • the troubleshooting process of FIG. 9 specifically includes the following steps S501 to S505.
  • the coordinates of the target image and the search result image matching feature point set are converted into the same coordinate system according to the calculated posture relationship.
  • the coordinates of the target image matching feature point set may be converted into the search result image coordinate system, or the coordinates of the search result image matching feature point set may be converted into the target image coordinate system.
  • the coordinate system conversion method refers to the description of step S201 in the foregoing embodiment.
  • the subset of the matched feature point pair set after the coordinate system conversion is divided, and several matching features are obtained.
  • Point to subset Each pair of matching feature point pairs includes a target image matching feature point subset and a retrieval result image matching feature point subset.
  • the specific subset division manner can be combined with FIG. 5, and the sub-set division description in step S303 is referred to.
  • the mode of the subset division in S503 is substantially the same as that of step S303, except that the manner of description is slightly different.
  • each of the target image matching feature point subset and the search result image matching feature point subset are respectively subjected to Delaunay triangulation to obtain a corresponding Delaunay triangular network.
  • Delaunay triangulation is performed for each subset, specifically: spatially sorting the coordinate points of the feature points in each subset according to the coordinate transformation, and constructing a Delaunay triangular network for each subset according to the sorting result.
  • the spatial ordering can be a median ordering.
  • the specific method of median sorting refers to the description of the median ordering in step S103 in the previous embodiment.
  • S505 comparing two Delaunay triangulation networks corresponding to the subset of matching feature points (a Delaunay triangular network corresponding to a target image matching feature point subset and a Delaunay triangular network corresponding to a search result image matching feature point subset) . If the matching feature point pair subset exceeds the preset ratio, the two triangle networks are satisfied. If the comparison result is consistent, it is determined that the image retrieval result is correct; otherwise, the image retrieval result is determined to be incorrect.
  • the preset ratio can be freely set according to the actual situation, and the setting range is preferably between 1/3 and 1/6. Assumption: The preset ratio is set to 2/3. At this time, if the subset of more than 2/3 matches the two triangular network alignment results, it is determined that the image retrieval result is correct.
  • FIG. 10 is a schematic diagram of a third structure of an image retrieval server according to an embodiment of the present invention.
  • the image retrieval server includes an image retrieval database 20 and a matching module 21.
  • the image retrieval database 20 stores feature point set data of a plurality of sample images.
  • the feature point set data of each sample image includes position information, scale, direction, and feature description information of each feature point in the feature point set in the image region.
  • the matching module 21 is configured to receive a retrieval request from the image retrieval client, and match the target image data included in the retrieval request in the image retrieval database according to the set matching algorithm to obtain one or more groups and target images.
  • the image retrieval server also includes a debug module 22.
  • the troubleshooting module 22 includes a posture calculation and coordinate system conversion unit 221, a triangular network construction unit 222, and a determination unit 223, wherein:
  • the posture calculation and coordinate system conversion unit 221 is configured to calculate a posture relationship between the target image and each of the retrieval result images by using the matching feature point pair scale and direction information according to the image retrieval result, and according to the calculated posture The relationship converts the coordinates of the target image and the search result image matching feature point set into the same coordinate system.
  • the coordinates of the target image matching feature point set may be converted into the search result image coordinate system, or the coordinates of the search result image matching feature point set may be converted into the target image coordinate system.
  • the coordinate system conversion method refers to the description of step S201 in the foregoing embodiment.
  • the triangular network construction unit 222 is configured to perform Delaunay triangulation on the target image matching feature point set and the retrieval result image matching feature point set respectively in the converted coordinate system, to obtain the Delaunay triangulation network corresponding to the target image and the retrieval result.
  • the image corresponds to the Delaunay triangle network.
  • the Delaunay triangulation is specifically as follows: matching the feature points in the feature points of the target image and the feature points in the image feature points of the search result, spatially sorting according to the coordinates converted by the coordinate system, and constructing corresponding Delaunay triangles according to the sorting results.
  • the internet The spatial ordering can be a median ordering.
  • the specific method of median sorting refers to the description of the median ordering in step S103 in the previous embodiment.
  • the determining unit 223 is configured to compare the two Delaunay triangulation networks. If the two triangle network comparison results are consistent, the image retrieval result is determined to be correct; otherwise, the image retrieval result is determined to be incorrect.
  • FIG. 11 is a schematic diagram of a fourth structure of an image retrieval server according to an embodiment of the present invention.
  • the image retrieval server structure differs from that of FIG. 10 in that the debug module 22 is different in structure.
  • a subset division unit 224 is added to the debug module 22, and the functions of the triangle network construction unit 225 and the determination unit 226 are also different from the corresponding unit functions in FIG. 10, as follows:
  • the posture calculation and coordinate system conversion unit 221 is completely consistent with the functions of the posture calculation and the coordinate system conversion unit in FIG. 10, and is used for calculating the target image and each by using the scale and direction information of the pair of matching feature points according to the image retrieval result.
  • a pose relationship between the search result images, and according to the calculated pose relationship, the coordinates of the target image matching feature point set and the search result image matching feature point set are converted into the same coordinate system.
  • the coordinates of the target image matching feature point set may be converted into the search result image coordinate system, or the coordinates of the search result image matching feature point set may be converted into the target image coordinate system.
  • the coordinate system conversion method refers to the description of step S201 in the foregoing embodiment.
  • the subset dividing unit 224 is configured to perform a subset of the matched feature point pair set after the coordinate system is converted according to the position information of the search result image corresponding to the target image matching feature point matching the feature point in the search result image. Dividing, a number of matching feature point pairs are obtained. Each pair of matching feature point pairs includes a target image matching feature point subset and a retrieval result image matching feature point subset.
  • the specific subset division manner can be combined with FIG. 5, and the sub-set division description in step S303 is referred to.
  • the mode of the subset division in S503 is substantially the same as that of step S303, except that the manner of description is slightly different.
  • the triangular network construction unit 225 is configured to perform Delaunay triangulation on each of the target image matching feature point subset and the search result image matching feature point subset in the converted coordinate system to obtain a corresponding Delaunay triangular network. Delaunay triangulation is performed for each subset. Specifically, the feature points in each subset are spatially sorted according to the coordinates after coordinate transformation, and a Delaunay triangulation network is constructed for each subset according to the sorting result.
  • the spatial ordering can be a median ordering.
  • the specific method of median sorting refers to the description of the median ordering in step S103 in the previous embodiment.
  • the determining unit 226 is configured to compare the two Delaunay triangulation networks corresponding to the subset of the matching feature points, and if the matched feature point pair subsets exceeding the preset ratio satisfy the consistency of the two triangular network comparison results, It is determined that the image retrieval result is correct; otherwise, the image retrieval result is determined to be incorrect.
  • the preset ratio can be freely set according to the actual situation, and the setting range is preferably between 1/3 and 1/6. Assumption: The preset ratio is set to 2/3. At this time, if the subset of more than 2/3 matches the two triangular network alignment results, it is determined that the image retrieval result is correct.
  • An embodiment of the present invention further provides an image retrieval method, including the following steps S601 to S604.
  • the extracted feature data includes position information, scale, direction, and feature description information of each feature point in the image area.
  • the preprocessed sample image refers to a sample image preprocessed by one or more methods of uniform size processing, redundant area culling, Gaussian blur processing, and affine transformation.
  • the extracted feature data is sent to the image retrieval server for image retrieval, and one or more search result images that are initially matched with the target image are obtained.
  • the search result is debugged by using the image search result troubleshooting method in any of the embodiments of the present invention.
  • An embodiment of the present invention further provides an image retrieval system including an image retrieval client and an image retrieval server.
  • the image retrieval client is installed on the mobile terminal.
  • the image retrieval server is an image retrieval server illustrated in any of Figs. 6, 7, 10, and 11.
  • the image retrieval client includes an image acquisition module, a feature extraction module, a retrieval request sending module, and a retrieval result receiving module.
  • the image acquisition module is configured to acquire a target image.
  • the feature extraction module is configured to perform feature extraction on the target image, and the extracted feature data includes location information, scale, direction, and feature description information of each feature point in the image region.
  • the search request sending module is configured to send the feature data extracted by the feature extraction module to the image retrieval server for image retrieval.
  • the search result receiving module is configured to receive the search result information returned from the image retrieval server.
  • the module or unit in the embodiment of the present invention may be implemented by a general-purpose integrated circuit, such as a CPU (Central Processing Unit) or an ASIC (Application Specific Integrated Circuit).
  • a general-purpose integrated circuit such as a CPU (Central Processing Unit) or an ASIC (Application Specific Integrated Circuit).
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

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Abstract

L'invention concerne une base de données d'extraction d'image, un procédé de génération de base de données d'extraction d'image, un serveur d'extraction d'image, un système d'extraction d'image et un procédé de résolution de problème de résultat d'extraction d'image. Dans le procédé, une relation interne d'un ensemble de points de caractéristique d'image est représentée par utilisation d'un réseau triangulaire de Delaunay, une résolution de problème (correction) est réalisée sur des résultats d'extraction au moyen de la caractéristique de caractère unique du réseau triangulaire de Delaunay, des résultats d'extraction corrects en termes d'algorithme (des lignes de base de conditions de contrainte sont satisfaites) mais erronés en termes de cognition humaine sont éliminés, et, en conséquence, le taux de précision des résultats d'extraction peut être efficacement amélioré.
PCT/CN2016/110341 2015-12-22 2016-12-16 Système d'extraction d'image, serveur, base de données et procédé associé WO2017107865A1 (fr)

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