Image coincidence verification method, electronic device and computer-readable storage medium

Abstract

The present application provides an image coincidence verification method, an electronic device, and a computer-readable storage medium. The present application obtains at least one intersection to be identified by obtaining the intersections between boundary lines in a first image, and then obtains the intersection attribute information of the at least one intersection to be identified and the intersection attribute information of at least one reference intersection in a second image, and then determines the coincidence between the first image and the second image based on the intersection attribute information of the at least one intersection to be identified and the intersection attribute information of the at least one reference intersection, so that the coincidence between the two images is recognized, so that images with repeated content can be recognized.

Description

[Technical field]

[0001] The present application relates to video processing technology, and in particular to an image coincidence verification method, an electronic device and a computer-readable storage medium. [Background technology]

[0002] At present, with the rapid development of Internet technology and the popularization of smart terminals, various forums, websites, and self-media platforms are constantly emerging. Video manufacturers and users generate a large number of videos and pictures with rich and diverse content every day, which are uploaded to various video websites, forums, self-media platforms, etc. for users to watch.

[0003] At the same time, pirated videos and pictures are also emerging in an endless stream, especially pictures, which are easily downloaded by some users from a certain website, forum, or self-media platform and then uploaded to other websites, forums, and self-media platforms. This results in websites, forums, and self-media platforms actually repeatedly collecting pictures with the same content, wasting storage resources; in addition, when recommending pictures to users, it is easy to recommend multiple pictures with the same content to users, causing users to watch pictures with duplicate content, which reduces the user experience.

[0004] In the prior art, it is generally determined whether the pictures are similar by comparing the names of the pictures, or by manually comparing the contents of the two pictures, and then determining whether a picture is a pirated picture. However, the names of pictures are easily modified. For some pirated pictures with modified names, it is difficult to identify pirated pictures by comparing the picture names, and the detection method of manually comparing the contents of two pictures is labor-intensive and inefficient. [Summary of the invention]

[0005] Multiple aspects of the present application provide an image coincidence verification method, an electronic device, and a computer-readable storage medium for determining content coincidence between images.

[0006] In one aspect of the present application, a method for checking image coincidence is provided, comprising:

[0007] Obtaining intersection points between boundary lines in the first image to obtain at least one intersection point to be identified;

[0008] Obtaining intersection attribute information of the at least one intersection to be identified, wherein the intersection attribute information of each of the at least one intersection to be identified includes: the number of intersecting boundary lines passing through each of the intersections to be identified, the deflection angle of each of the intersecting boundary lines passing through each of the intersections to be identified, and the distance between each of the intersections to be identified and the nearest intersection on each of the intersection boundary lines;

[0009] Acquire intersection attribute information of at least one reference intersection in the second image;

[0010] Based on the intersection attribute information of the at least one to-be-identified intersection and the intersection attribute information of the at least one reference intersection, the degree of coincidence between the first image and the second image is determined.

[0011] Another aspect of the present application provides an electronic device, the electronic device comprising:

[0012] one or more processors;

[0013] a storage device for storing one or more programs,

[0014] When the one or more programs are executed by the one or more processors, the one or more processors implement the image coincidence recognition method provided in the above aspect.

[0015] In another aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the image coincidence recognition method provided in the above aspect is implemented.

[0016] It can be seen from the above technical solution that the embodiment of the present application obtains at least one intersection to be identified by obtaining the intersection between the boundary lines in the first image, and then obtains the intersection attribute information of the at least one intersection to be identified and the intersection attribute information of at least one reference intersection in the second image. Furthermore, based on the intersection attribute information of the at least one intersection to be identified and the intersection attribute information of the at least one reference intersection, the degree of overlap between the first image and the second image is determined. In this way, the recognition of the overlap between the two images is realized, so that images with repeated content can be identified.

[0017] In addition, by adopting the technical solution provided in the present application, since it is possible to determine the degree of overlap between two images based on the intersection attribute information between the boundary lines in the two images, thereby identifying images with duplicate content, it is possible to avoid websites, forums, and self-media platforms from repeatedly collecting images with the same content, thereby saving storage resources and maintenance resources.

[0018] In addition, by adopting the technical solution provided in the present application, since it is possible to determine the degree of overlap between two images based on the intersection attribute information between the intersection points of the boundary lines in the two images, thereby identifying images with duplicate content, it is possible to avoid recommending multiple images with the same content to users when recommending images to users, thereby avoiding users from viewing pictures with the same content, and effectively improving the user experience.

[0019] In addition, the technical solution provided by the present application reduces labor costs and improves recognition efficiency compared to detection methods that determine whether images are similar by manually comparing the contents of two images.

Brief Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1 A schematic diagram of a flow chart of an image coincidence verification method provided in one embodiment of the present application;

[0022] Figure 2 An example diagram of an image received and a first image in an embodiment of the present application;

[0023] Figure 3 for Figure 1 A schematic diagram of a process for obtaining the intersection attribute information of at least one intersection to be identified in the illustrated embodiment;

[0024] Figure 4 A schematic diagram of a process for determining the degree of overlap between the first image and the second image in an embodiment of the present application;

[0025] Figure 5 for Figure 4 A schematic diagram of a process of obtaining a similarity matrix of a first attribute matrix of at least one intersection to be identified in the illustrated embodiment;

[0026] Figure 6 for Figure 4 A schematic diagram of a process of determining the similarity between the first attribute matrix set and the second attribute matrix set in the illustrated embodiment;

[0027] Figure 7 A schematic diagram of a flow chart of an image coincidence verification method provided by another embodiment of the present application;

[0028] Figure 8 FIG. 1 is a block diagram of an exemplary computer system / server 12 suitable for implementing embodiments of the present application. [Specific implementation method]

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] It should be noted that the terminals involved in the embodiments of the present application may include but are not limited to mobile phones, personal digital assistants (PDAs), wireless handheld devices, tablet computers, personal computers (PCs), MP3 players, MP4 players, wearable devices (for example, smart glasses, smart watches, smart bracelets, etc.), etc.

[0031] In addition, the term "and / or" in this article is only a description of the association relationship between the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0032] As described in the background technology, various pictures can be easily downloaded by some users from a certain website, forum, or self-media platform and then uploaded to other websites, forums, or self-media platforms. In this way, websites, forums, or self-media platforms actually repeatedly collect pictures with the same content, wasting storage resources. In addition, when recommending pictures to users, it is easy to recommend multiple pictures with the same content to users, causing users to watch pictures with duplicate content, which reduces user experience. However, the method of judging whether pictures are similar by comparing the names between pictures is difficult to identify pirated pictures by comparing the names of pictures, because the names of pictures are easily modified. The detection method of judging whether pictures are similar by manually comparing the contents of two pictures is labor-intensive and inefficient.

[0033] Therefore, there is an urgent need to provide an image coincidence verification method to determine the content coincidence between two images.

[0034] Figure 1 A schematic diagram of a flow chart of an image coincidence verification method provided in an embodiment of the present application, such as Figure 1 shown.

[0035] 101. Obtain intersection points between boundary lines in a first image to obtain at least one intersection point to be identified.

[0036] The at least one intersection point to be identified is an intersection point between boundary lines in the first image.

[0037] Optionally, the first image in the embodiment of the present application refers to an image that needs to be identified as to whether it is repeated with other image contents.

[0038] In addition, the first image in the embodiment of the present application can be a picture of any format and content, for example, it can be an image in the format of bmp, jpg, png, tif, gif, pcx, tga, exif, fpx, svg, psd, cdr, pcd, dxf, ufo, eps, ai, raw, WMF, webp, avif, etc.

[0039] In addition, the first image in the embodiment of the present application can be a separate picture or a picture captured from a video, and the embodiment of the present application does not limit this.

[0040] In addition, the boundary lines in the first image in the embodiment of the present application may include one boundary line, any number of boundary lines, or all boundary lines in the first image. The boundary lines refer to the edge lines, contour lines, and other lines of the various parts of the content (people, objects, etc.) in the first image.

[0041] 102. Obtain intersection attribute information of the at least one intersection to be identified.

[0042] Among them, the intersection attribute information of each of the at least one intersection to be identified includes: the number of intersecting boundary lines passing through each of the intersections to be identified, the deflection angle of each of the intersecting boundary lines passing through each of the intersections to be identified, and the distance between each of the intersections to be identified and the nearest intersection on each of the intersecting boundary lines.

[0043] 103. Obtain intersection attribute information of at least one reference intersection in the second image.

[0044] The at least one reference intersection point is an intersection point between boundary lines in the second image.

[0045] Optionally, the second image in the embodiment of the present application refers to any image used to compare with the first image to identify whether the content of the first image is repeated therewith.

[0046] In addition, the second image in the embodiment of the present application can be a picture of any format and content, for example, it can be an image in the format of bmp, jpg, png, tif, gif, pcx, tga, exif, fpx, svg, psd, cdr, pcd, dxf, ufo, eps, ai, raw, WMF, webp, avif, etc.

[0047] In addition, the second image in the embodiment of the present application can be a separate picture or a picture captured from a video, and the embodiment of the present application does not limit this.

[0048] In addition, the boundary lines in the second image in the embodiment of the present application may include one boundary line, any number of boundary lines, or all boundary lines in the second image. The boundary lines refer to the edge lines, contour lines, and other lines of the various parts of the content (people, objects, etc.) in the second image.

[0049] 104. Determine a degree of overlap between the first image and the second image based on intersection attribute information of the at least one to-be-identified intersection and intersection attribute information of the at least one reference intersection.

[0050] It should be noted that part or all of the execution entities of 101 to 104 may be applications located in the terminal, or may also be functional units such as plug-ins or software development kits (SDKs) in the applications that set the terminal, or may also be applications located in the network side server, and the embodiments of the present application do not specifically limit this.

[0051] It is understandable that the application may be a local program (nativeApp) installed on the terminal, or may be a web page program (webApp) of a browser on the terminal, which is not limited in the embodiments of the present application.

[0052] In this way, the recognition of the overlap between two images is achieved, so that images with duplicate content can be identified.

[0053] In addition, by adopting the technical solution provided in the present application, since it is possible to determine the degree of overlap between two images based on the intersection attribute information between the boundary lines in the two images, thereby identifying images with duplicate content, it is possible to avoid websites, forums, and self-media platforms from repeatedly collecting images with the same content, thereby saving storage resources and maintenance resources.

[0054] In addition, by adopting the technical solution provided in the present application, since it is possible to determine the degree of overlap between two images based on the intersection attribute information between the intersection points of the boundary lines in the two images, thereby identifying images with duplicate content, it is possible to avoid recommending multiple images with the same content to users when recommending images to users, thereby avoiding users from viewing pictures with the same content, and effectively improving the user experience.

[0055] In addition, the technical solution provided by the present application reduces labor costs and improves recognition efficiency compared to detection methods that determine whether images are similar by manually comparing the contents of two images.

[0056] The embodiments of the present application can be applied to any website, forum, self-media platform, etc., to identify the content overlap between the image uploaded by the user and the existing images in the database. At this time, the first image is the image uploaded by the user or the image obtained by performing some processing (such as sharpening, black and white, etc.) on the uploaded image, and the second image is any image stored in the database of the website, forum, self-media platform, etc. By comparing the first image with all the images stored in the database one by one, it is possible to identify whether the image uploaded by the user has the same content as the existing images in the database.

[0057] In addition, the embodiments of the present application can be applied to a terminal device to compare whether the contents of any two images are repeated.

[0058] Optionally, in some implementations, before 101, the received image may be sharpened to obtain the first image.

[0059] Among them, image sharpening is to compensate for the contour of the image, enhance the edge of the image and the grayscale jump part, so that the image becomes clear, which is divided into two categories: spatial domain processing and frequency domain processing. Image sharpening is to highlight the edges, contours, or characteristics of certain linear target elements of the objects on the image. This filtering method increases the contrast between the edge of the object and the surrounding pixels, so it is also called edge enhancement. In the embodiment of the present application, any image sharpening method, such as first-order differential, second-order differential, etc., can be used to sharpen the received image. The embodiment of the present application does not limit the specific image sharpening method used.

[0060] Based on this embodiment, by sharpening the received image, the edge lines and contour lines of the objects in the image can be made clearer, which helps to obtain clearer boundary lines. Since the similarity between the boundary lines in the two images can be more accurately determined, the overlap between the two images can be more accurately determined, thereby improving the recognition effect of the repeatability of the image content.

[0061] Alternatively, in some other implementations, before step 101, the received image may be sharpened and converted to black and white in sequence to obtain the first image.

[0062] Among them, black and white conversion, also known as grayscale conversion, is to process the image in black and white to make it lose color. Starting from the pixel point, it is to make the values ​​of the three color components of red, green and blue (R, G, B) of each pixel point the same. The embodiment of the present application can adopt any black and white conversion method, such as the commonly used maximum method (Maximum), average method (Average), weighted average method (WeightedAverage), etc. The embodiment of the present application is not limited to a specific black and white conversion method.

[0063] Alternatively, in some other implementations, before step 101, the received image may be converted to black and white and sharpened in sequence to obtain the first image.

[0064] Figure 2 is an example diagram of the received image and the first image in the embodiment of the present application. Figure 2 As shown, the left image is the received image (also called the original image), and the right image is the first image obtained by sequentially sharpening and black-and-whitening the left image. The white edge lines of the brim, shoulders, hair, etc. in the right image are the boundary lines.

[0065] Based on this embodiment, the image is black and whited on the basis of image sharpening, so that the values ​​of the three color components of each pixel are the same, which helps to more accurately and quickly determine the edge lines and contour lines of the objects in the image, so as to improve the accuracy and efficiency of boundary line acquisition.

[0066] Optionally, in some of the implementations, before 101, a rectangular boundary area of ​​the boundary line in the first image, that is, a circumscribed rectangular area of ​​the boundary line in the first image, may be obtained, and then, based on a preset size, and the length and height of the rectangular boundary area, the boundary line in the first image may be proportionally scaled along two coordinate axis directions in a preset plane coordinate system.

[0067] Based on this embodiment, a rectangular boundary area of ​​the boundary line in the first image is obtained, and based on the length and height of the rectangular boundary area, the boundary line in the first image is proportionally scaled along the two coordinate axis directions in the preset plane coordinate system with a preset size as the standard. Without changing the shape of the boundary line, the intersection attributes, etc., the boundary line is proportionally scaled in two directions to the preset standard size. This can avoid the inability to accurately obtain the intersection attribute information of the intersections between the boundary lines due to the small size of the first image, and can also avoid the large size of the first image resulting in inconsistency with the standard of the intersection attribute information in the second image, thereby affecting the final coincidence recognition result, thereby improving the accuracy of the coincidence recognition results between images.

[0068] Optionally, in some of the implementations, in 101, the intersection points between the boundary lines in the first image can be obtained specifically in the following manner to obtain at least one intersection point to be identified: for example, in one of the methods, a boundary line with the longest length and a boundary line intersecting with the boundary line with the longest length are selected from the first image, and the intersection points on the boundary line with the longest length and the intersection points on the boundary line intersecting with the boundary line with the longest length are obtained.

[0069] Alternatively, in another manner, at least one boundary line is selected from the first image in descending order of length, and an intersection point on the at least one boundary line is obtained.

[0070] Alternatively, in yet another embodiment, the intersection points between all boundary lines in the first image are obtained.

[0071] The embodiment of the present application does not limit the method of selecting at least one intersection to be identified from the first image, and it can be set according to specific needs.

[0072] Since longer boundary lines correspond to larger contours of objects in the image, they can better reflect the content of the image as a whole. Based on this embodiment, longer boundary lines are selected from the image, or intersection points on intersecting boundary lines are further selected to determine the degree of overlap between the image and other images, which helps to improve the accuracy of image overlap recognition results.

[0073] Figure 3 for Figure 1 A schematic diagram of a process for obtaining the intersection attribute information of at least one intersection to be identified in the embodiment shown. Figure 3 As shown, based on the above embodiment, 102 includes:

[0074] 301. Obtain the number of intersections of the at least one intersection to be identified.

[0075] 302. For each of the intersections to be identified, obtain the number n of intersecting boundary lines passing through each of the intersections to be identified; based on a preset plane coordinate system, obtain the deflection angle a of each of the intersecting boundary lines passing through each of the intersections to be identified in a predetermined rotation direction and a direction of one of the predetermined coordinate axes (for example, the X-axis) in the preset plane coordinate system; obtain the straight-line distance s between each of the intersections to be identified and the nearest intersection on each of the intersecting boundary lines.

[0076] Optionally, in some implementations, when obtaining the number n of intersecting boundary lines passing through an intersection to be identified, the skew angle a and the straight-line distance s, each boundary line takes the intersection as the starting point, and the line segment passing through an intersection is calculated independently as two boundary lines.

[0077] Optionally, in some implementations, obtaining the straight-line distance s between the intersection point to be identified and the nearest intersection point on each intersecting boundary line instead of the total length of the intersecting boundary line can speed up the calculation efficiency. The straight-line distance s can be calculated using the following formula:

[0078] s=sqrt((x1-x2)^2+(y1-y2)^2)

[0079] Among them, (x1, y1) and (x2, y2) are the coordinates of each intersection point to be identified and the nearest intersection point on each intersecting boundary line.

[0080] Optionally, in some implementations, when obtaining the straight-line distance s between the intersection point to be identified and the nearest intersection point on each intersecting boundary line, if there is no next intersection point on the intersecting boundary line, the final position of the intersecting boundary line may be used for calculation.

[0081] Optionally, in some of the implementations, when obtaining the deflection angle a of each intersecting boundary line according to a predetermined rotation direction and a predetermined coordinate axis direction in a preset plane coordinate system, the deflection angle a may be calculated using the following formula:

[0082] Tag(a)=(y2-y1) / (x2-x1)

[0083] 303. For each intersecting boundary line, obtain a triple consisting of the number n of boundary lines passing through each intersection to be identified, the deflection angle a, and the straight-line distance s, and each boundary line passing through each intersection to be identified corresponds to a triple (n, a, s).

[0084] 304. Construct a first attribute matrix for each of the intersections to be identified based on the triplets corresponding to all intersecting boundary lines passing through each of the intersections to be identified.

[0085] In a specific example, the first attribute matrix of each intersection to be identified can be expressed as:

[0086] |n a1 s1|

[0087] |n a2 s2|

[0088] |n a3 s3|

[0089] |…………|

[0090] The intersection attribute information of each to-be-identified intersection is specifically a first attribute matrix of each to-be-identified intersection.

[0091] 305. Obtain a set of first attribute matrices of the at least one intersection to be identified, to obtain a first attribute matrix set.

[0092] The intersection attribute information of the at least one intersection to be identified is specifically the first attribute matrix set.

[0093] Continuing with the above specific example, the above processes 302 to 304 are sequentially performed on other intersections to be identified, and the first attribute matrix of the at least one intersection to be identified forms a matrix set, namely, a first attribute matrix set.

[0094] Optionally, in some of the implementations, the corresponding element values ​​in other triplets in the first attribute matrix set may be proportionally converted based on the element value in the triplet corresponding to one of the intersecting boundary lines.

[0095] In actual applications, the image may be stretched, rotated, compressed or enlarged, resulting in inconsistency in the actual pixel points, so that the directly obtained skew angle a and straight-line distance s will show differences due to the stretching, rotation, compression or enlargement of the image. In view of the above situation, after the first attribute matrix is ​​constructed from the triplets or after the first attribute matrix set is further obtained, the corresponding element values ​​in other triplets in the first attribute matrix set are proportionally converted based on the element value in the triplet corresponding to an intersecting boundary line, and the element values ​​in the triplets in the first attribute matrix set are normalized, which can effectively eliminate the differences in the skew angle a and straight-line distance s caused by the stretching, rotation, compression or enlargement of the image, so that the intersection attribute information of each intersection to be identified is more accurate.

[0096] For example, continuing above Figure 3 In the specific example of the embodiment shown, the first attribute matrix is ​​taken as an example, and the first row in the first attribute matrix is ​​used as a reference to perform proportional conversion on the corresponding element values ​​in the triples of the following rows. As follows:

[0097]

[0098]

[0099] Similarly, the same method can be used to proportionally convert the corresponding element values ​​in each triplet in other first attribute matrices in the first attribute matrix set, and the embodiments of the present application will not be repeated.

[0100] Optionally, in some of the implementations, in 103, a second attribute matrix set of at least one reference intersection in the second image stored in the database may be obtained. The second attribute matrix set is a set of second attribute matrices of the at least one reference intersection; the second attribute matrix is ​​constructed by a triple consisting of the number of intersecting boundary lines passing through the reference intersection, the deflection angle of each intersecting boundary line passing through the reference intersection in a predetermined rotation direction and one of the predetermined coordinate axis directions in the preset plane coordinate system, and the straight-line distance between the reference intersection and the nearest intersection point on each intersecting boundary line passing through the reference intersection.

[0101] Alternatively, in some other implementations, in 103, a method similar to Figure 3 In the embodiment shown, obtaining the intersection attribute information of at least one reference intersection in the second image specifically includes:

[0102] Obtaining the number of intersections of the at least one reference intersection;

[0103] For each of the reference intersection points, respectively, the number of intersecting boundary lines passing through the reference intersection point is obtained; based on a preset plane coordinate system, the deflection angle of each intersecting boundary line passing through the reference intersection point in a predetermined rotation direction and a direction of one of the predetermined coordinate axes in the preset plane coordinate system is obtained; the straight-line distance between the reference intersection point and the nearest intersection point on each intersecting boundary line passing through the reference intersection point is obtained;

[0104] For each intersecting boundary line passing through the reference intersection, respectively, a triplet consisting of the number of boundary lines passing through the reference intersection, the deflection angle of each intersecting boundary line passing through the reference intersection with one of the predetermined coordinate axis directions in the preset plane coordinate system according to a predetermined rotation direction, and the straight-line distance between the reference intersection and the nearest intersection point on each intersecting boundary line passing through the reference intersection;

[0105] A second attribute matrix of the reference intersection is constructed based on the triplets corresponding to all intersecting boundary lines passing through the reference intersection, wherein the intersection attribute information of the reference intersection is specifically the second attribute matrix of the reference intersection;

[0106] A set of second attribute matrices of the at least one reference intersection is acquired to obtain a second attribute matrix set, wherein the intersection attribute information of the at least one reference intersection is specifically the second attribute matrix set.

[0107] Optionally, in some implementations, the element value in each triplet in the second attribute matrix set is specifically the element value that is proportionally converted based on the element value in the triplet corresponding to one of the intersecting boundary lines passing through the reference intersection point.

[0108] In the embodiment of the present application, the intersection attribute information of the at least one intersection to be identified can be obtained in advance by using the above embodiment to obtain the intersection attribute information of at least one reference intersection in the second image, and stored in the database corresponding to the second image. Alternatively, the intersection attribute information of the at least one intersection to be identified can be obtained by using the above embodiment to obtain the intersection attribute information of at least one reference intersection in the real-time second image. The embodiment of the present application does not limit this.

[0109] Figure 4 FIG. 1 is a flow chart of determining the degree of overlap between the first image and the second image in an embodiment of the present application. Figure 4 As shown, based on the above embodiment, 104 includes:

[0110] 401. Obtain a similarity matrix of the first attribute matrix of the at least one intersection to be identified from the second attribute matrix set.

[0111] 402. Determine the similarity between the first attribute matrix set and the second attribute matrix set based on the number of similarity matrices of the first attribute matrix of the at least one intersection to be identified and the similarity between the first attribute matrix of the at least one intersection to be identified and the corresponding similarity matrix.

[0112] 403. Determine a degree of overlap between the first image and the second image based on a similarity between the first attribute matrix set and the second attribute matrix set.

[0113] For example, the similarity between the first attribute matrix set and the second attribute matrix set can be directly used as the overlap between the first image and the second image, or based on a predetermined correspondence between the similarity between the attribute matrix sets and the overlap between the images, the overlap corresponding to the similarity between the first attribute matrix set and the second attribute matrix set can be obtained as the overlap between the first image and the second image.

[0114] Based on this embodiment, the intersection attribute information of the intersection points in the two images is converted into a matrix for similarity calculation. By determining the similarity between the first attribute matrix set and the second attribute matrix set, the overlap between the first image and the second image is determined, which can improve the similarity calculation performance and thus improve the overall image overlap recognition efficiency.

[0115] Figure 5 for Figure 4 The embodiment shown is a schematic diagram of a process for obtaining a similarity matrix of the first attribute matrix of at least one intersection to be identified. Figure 5 As shown, in Figure 4 Based on the illustrated embodiment, 401 includes:

[0116] 501. Select a first attribute matrix with the largest dimension in the first attribute matrix set.

[0117] 502. Taking the dimension of the first attribute matrix with the largest dimension as the first dimension, obtaining a second attribute matrix with the dimension of the first dimension from the second attribute matrix set, and obtaining at least one first target second attribute matrix.

[0118] 503. Obtain difference matrices between the first attribute matrix with the largest dimension and each second attribute matrix in the at least one first target second attribute matrix, respectively, to obtain a first difference matrix set.

[0119] For example, continuing above Figure 3 In the specific example of the embodiment shown, first, from the first attribute matrix set, a first attribute matrix A with the largest dimension is selected, and the dimension of the first attribute matrix A is determined (expressed as A0), and then a second attribute matrix B with a dimension of A0 is queried from the second attribute matrix set, and multiple second attribute matrices B may be queried, and the queried second attribute matrix B is the at least one first target second attribute matrix. The element values ​​of each element in the first attribute matrix A and the first second attribute matrix B are taken out in turn according to the dimension, and the difference between the corresponding element values ​​is calculated to obtain the difference matrix M between the first attribute matrix A and the first second attribute matrix B, which can be expressed as:

[0120] |m11 m12 m13|

[0121] |m21 m22 m23|

[0122] |m31 m32 m33|

[0123] |………|

[0124] If there are multiple second attribute matrices B, for other second attribute matrices B, the above method is used for calculation in turn to obtain the difference matrix M between the first attribute matrix A and each second attribute matrix B, and the difference matrix set M1 is obtained from the set of difference matrices between the first attribute matrix A and all queried second attribute matrices B.

[0125] 504. Take the to-be-identified intersection corresponding to the first attribute matrix with the largest dimension as the first to-be-identified intersection, and obtain a second to-be-identified intersection among the at least one to-be-identified intersection, which is connected to the first to-be-identified intersection by a boundary line and whose corresponding first attribute matrix has the largest dimension in the first attribute matrix set.

[0126] For example, in a specific example, assuming that the first intersection to be identified corresponding to the first attribute matrix with the largest dimension selected in 501 is P1, taking P1 as the starting point, obtain the second intersection P2 to be identified among the at least one intersection to be identified, which has a boundary line connection with P1 (i.e., connectivity) and whose corresponding first attribute matrix has the largest dimension in the first attribute matrix set.

[0127] 505. Taking the dimension of the first attribute matrix of the second intersection to be identified as the second dimension, obtain a second attribute matrix with the dimension of the second dimension from the second attribute matrix set to obtain at least one candidate second attribute matrix.

[0128] Continuing with the example in 504 , assume that the first attribute matrix of the second intersection to be identified is C; perform operation 502 for P2 to query and obtain at least one candidate second attribute matrix D of the second dimension, and the corresponding reference intersection is denoted as Q2 .

[0129] 506. Use the reference intersection corresponding to the at least one first target second attribute matrix as the first reference intersection, use the reference intersection corresponding to the at least one candidate second attribute matrix as the second reference intersection, select a candidate second attribute matrix of the second reference intersection having a boundary line connection relationship with the first reference intersection from the at least one candidate second attribute matrix, and obtain at least one second target second attribute matrix.

[0130] Specifically, in the examples of 504 to 505, assuming that the reference intersection point corresponding to the at least one first target second attribute matrix is ​​represented as Q1, the connectivity between Q1 and Q2 is determined. Since P1 and P2 are connected, Q1 and Q2 should also be connected. The candidate second attribute matrices without connectivity corresponding to Q2 that has no connectivity with Q1 are eliminated to obtain at least one second target second attribute matrix.

[0131] 507. Obtain a difference matrix between the first attribute matrix of the second intersection to be identified and each second attribute matrix in the at least one second target attribute matrix, respectively, to obtain a second difference matrix set, which can be expressed as M2.

[0132] 508. Take the second intersection point to be identified as the first intersection point to be identified, and start iteratively executing operations 504 to 507 until no second intersection point to be identified that meets the condition in 504 is obtained, thereby obtaining a second difference matrix set N of the at least one intersection point to be identified.

[0133] After the above processing, a similarity matrix N is obtained for representing the similarity between the at least one intersection to be identified and the midpoint and the midpoint of the at least one reference intersection, which can be expressed as:

[0134] |p1 r1 M1|

[0135] |p2 r2 M2|

[0136] |p3 r3 M3|

[0137] |………|

[0138] Wherein, pi represents the intersection to be identified, ri represents the number of second difference matrices in the second difference matrix set of the intersection to be identified, Mi represents the second difference matrix set, and i=1, 2, 3,….

[0139] Based on this embodiment, the similarity matrix of the first attribute matrix of at least one intersection to be identified is screened out through the connectivity between the intersections to determine the similarity between the first attribute matrix set and the second attribute matrix set, and further determine the overlap between the first image and the second image. The second attribute matrices corresponding to dissimilar reference intersections can be eliminated, thereby greatly reducing the amount of calculation and improving calculation efficiency.

[0140] Figure 6 for Figure 4 A schematic diagram of a process for determining the similarity between the first attribute matrix set and the second attribute matrix set in the embodiment shown. Figure 6 As shown, in Figure 4 or Figure 5 Based on the illustrated embodiment, 402 includes:

[0141] 601. Take the first intersection to be identified and each of the second intersections to be identified as target intersections to be identified, and determine the similarity of the target intersections to be identified based on the skew angles and straight-line distances of each dimension in the first attribute matrix of the target intersections to be identified, and the weights of the first difference matrix set or the second difference matrix set corresponding to the target intersections to be identified.

[0142] For example, continuing above Figure 5 In the specific example of the embodiment shown, Mi can be processed into similarity in the following manner:

[0143] u=Σ(ai+si) / Mi

[0144] Wherein, ai and si represent the skew angle a and the straight-line distance s in the triplet of the first attribute matrix corresponding to the intersection to be identified. Then the similarity matrix N in 508 becomes:

[0145] |p1 r1 u1|

[0146] |p2 r2 u2|

[0147] |p3 r3 u3|

[0148] |………|

[0149] 602. Construct a similarity matrix based on the number and similarity of the second attribute matrices in the at least one first target second attribute matrix corresponding to the first intersection to be identified, and the number and similarity of the second attribute matrices in the at least one second target second attribute matrix corresponding to each second intersection to be identified.

[0150] 603. Perform weighted averaging of the similarities of each dimension based on the number of second attribute matrices of each dimension in the similarity matrix to obtain the similarity between the first attribute matrix set and the second attribute matrix set.

[0151] For example, continuing the specific example in 601 above, the similarity of each dimension can be weighted averaged by the following formula to obtain the similarity result between the first attribute matrix set and the second attribute matrix set:

[0152] result=Σ(ri*ui) / Σ(ri).

[0153] Based on this embodiment, the similarity between the first attribute matrix set and the second attribute matrix set can be accurately determined by matrix calculation, thereby accurately determining the overlap between the first image and the second image.

[0154] Figure 7 A flowchart of an image coincidence verification method provided by another embodiment of the present application is shown in FIG. Figure 7 shown.

[0155] 701. Receive an image upload request, where the image upload request is used to request uploading of the first image.

[0156] The image upload request may be specifically initiated by the user to a website, forum, self-media platform or other platform when requesting to upload the first image.

[0157] 702. Obtain intersection points between boundary lines in the first image to obtain at least one intersection point to be identified.

[0158] 703. Obtain intersection attribute information of the at least one intersection to be identified.

[0159] Among them, the intersection attribute information of each of the at least one intersection to be identified includes: the number of intersecting boundary lines passing through each intersection to be identified, the deflection angle of each intersecting boundary line, and the distance between each intersection to be identified and the nearest intersection on each intersecting boundary line.

[0160] 704. Obtain intersection attribute information of at least one reference intersection in the second image.

[0161] 705. Determine a degree of overlap between the first image and the second image based on the intersection attribute information of the at least one intersection to be identified and the intersection attribute information of the at least one reference intersection.

[0162] 706. Identify whether the degree of overlap between the first image and the second image is lower than a preset threshold.

[0163] If the degree of overlap between the first image and the second image is lower than a preset threshold, it can be considered that the first image and the second image do not overlap, and execute 707; otherwise, if the degree of overlap between the first image and the second image is not lower than the preset threshold, it can be considered that the first image and the second image overlap, and execute 708.

[0164] 707. Store the first image and the intersection attribute information of the at least one intersection to be identified in a database.

[0165] 708. Output a prompt message indicating that the image upload failed.

[0166] After the first image and the intersection attribute information of the at least one intersection to be identified are stored in the database, the first image can be used as the second image in the future to compare the degree of overlap with the image requested to be uploaded by the user.

[0167] Based on this embodiment, when a user requests to upload an image, it is possible to promptly identify whether an image with the same content as the image requested to be uploaded by the user already exists in the database, thereby avoiding storing images with the same content, saving storage resources and maintenance resources, and avoiding subsequently recommending multiple images with the same content to other users, which can effectively improve the user experience.

[0168] The technical solution of the present application can be applied to image processing applications in terminals, servers and other devices. When the image coincidence verification method provided by the embodiment of the present application is executed by using an image processing application, the coincidence between two images can be recognized, thereby being able to identify images with duplicate content.

[0169] In addition, by adopting the technical solution provided by this application,

[0170] Since the degree of overlap between two images can be determined based on the intersection attribute information between the intersection points of the boundary lines in the two images, images with duplicate content can be identified. This can prevent websites, forums, and self-media platforms from repeatedly collecting images with the same content, saving storage and maintenance resources.

[0171] In addition, by adopting the technical solution provided in the present application, since it is possible to determine the degree of overlap between two images based on the intersection attribute information between the intersection points of the boundary lines in the two images, thereby identifying images with duplicate content, it is possible to avoid recommending multiple images with the same content to users when recommending images to users, thereby avoiding users from viewing pictures with the same content, and effectively improving the user experience.

[0172] In addition, the technical solution provided by the present application reduces labor costs and improves recognition efficiency compared to detection methods that determine whether images are similar by manually comparing the contents of two images.

[0173] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described action sequence, because according to the present application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0174] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] Figure 8 A block diagram of an exemplary computer system / server 12 suitable for use in implementing embodiments of the present application is shown. Figure 8 The computer system / server 12 shown is merely an example and should not limit the functionality and scope of use of the embodiments of the present application.

[0176] like Figure 8 As shown, computer system / server 12 is represented in the form of a general-purpose computing device. Components of computer system / server 12 may include, but are not limited to: one or more processors or processing units 16, storage devices or system memory 28, and buses 18 that connect different system components (including system memory 28 and processing units 16).

[0177] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0178] Computer system / server 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer system / server 12, including volatile and nonvolatile media, removable and non-removable media.

[0179] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The computer system / server 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 8 not shown, usually called a "hard drive"). Although Figure 8 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0180] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0181] The computer system / server 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), one or more devices that enable a user to interact with the computer system / server 12, and / or any device that enables the computer system / server 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 44. In addition, the computer system / server 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the computer system / server 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the computer system / server 12, including, but not limited to, microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0182] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing Figure 1 to Figure 6 The image coincidence verification method provided by any one of the corresponding embodiments.

[0183] Another embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements Figure 1 to Figure 6 The image coincidence verification method provided by any one of the corresponding embodiments.

[0184] Specifically, any combination of one or more computer-readable media can be used. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0185] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0186] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0187] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0188] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0189] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or page components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0190] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0192] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0193] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for checking image coincidence, characterized in that: include: According to the length of the boundary line, obtaining the intersection point between the boundary lines in the first image to obtain at least one intersection point to be identified; Obtaining intersection attribute information of the at least one intersection to be identified, wherein the intersection attribute information of each of the at least one intersection to be identified includes: the number of intersecting boundary lines passing through each of the intersections to be identified, the deflection angle of each of the intersecting boundary lines passing through each of the intersections to be identified, and the distance between each of the intersections to be identified and the nearest intersection on each of the intersecting boundary lines, wherein the intersection attribute information of each of the intersections to be identified is a first attribute matrix, and the intersection attribute information of the at least one intersection to be identified is a first attribute matrix set; Acquire intersection attribute information of at least one reference intersection in the second image, where the intersection attribute information of the at least one reference intersection is a second attribute matrix set; determining a degree of overlap between the first image and the second image based on the intersection attribute information of the at least one to-be-identified intersection and the intersection attribute information of the at least one reference intersection; Wherein, determining the degree of overlap between the first image and the second image based on the intersection attribute information of the at least one to-be-identified intersection and the intersection attribute information of the at least one reference intersection includes: Obtaining a similarity matrix of the first attribute matrix of the at least one intersection to be identified from the second attribute matrix set; Determine the similarity between the first attribute matrix set and the second attribute matrix set based on the number of similarity matrices of the first attribute matrix of the at least one intersection to be identified and the similarity between the first attribute matrix of the at least one intersection to be identified and the corresponding similarity matrix; Based on the similarity between the first attribute matrix set and the second attribute matrix set, the coincidence degree between the first image and the second image is determined.

2. The method according to claim 1, characterized in that Before obtaining the intersection points between the boundary lines in the first image, the method further includes: sharpening the received image to obtain the first image; or, sequentially sharpening and black-and-whitening the received image to obtain the first image; or, The received image is sequentially converted to black and white and sharpened to obtain the first image.

3. The method according to claim 1, characterized in that: Before obtaining the intersection points between the boundary lines in the first image, the method further includes: Acquire a rectangular boundary area of ​​a boundary line in the first image; Based on the preset size, and the length and height of the rectangular boundary area, the boundary line in the first image is proportionally scaled along two coordinate axis directions in a preset plane coordinate system.

4. The method according to claim 1, characterized in that The step of obtaining the intersection points between the boundary lines in the first image according to the lengths of the boundary lines includes: Selecting from the first image a boundary line with the longest length and a boundary line intersecting with the boundary line with the longest length, and obtaining intersection points on the boundary line with the longest length and intersection points on the boundary line intersecting with the boundary line with the longest length; or, At least one boundary line is selected from the first image in descending order of length, and an intersection point on the at least one boundary line is obtained.

5. The method according to any one of claims 1 to 4, characterized in that: The obtaining the intersection attribute information of the at least one intersection to be identified includes: Obtaining the number of intersections of the at least one intersection to be identified; For each of the intersection points to be identified, respectively, the number of intersecting boundary lines passing through each of the intersection points to be identified is obtained; based on a preset plane coordinate system, the deflection angle of each of the intersecting boundary lines in a predetermined rotation direction and a direction of one of the predetermined coordinate axes in the preset plane coordinate system is obtained; and the straight-line distance between each of the intersection points to be identified and the nearest intersection point on each of the intersecting boundary lines is obtained; For each of the intersecting boundary lines, respectively, a triple consisting of the number of boundary lines passing through each of the intersection points to be identified, the deflection angle, and the straight-line distance is obtained; A first attribute matrix of each of the intersections to be identified is constructed based on the triplets corresponding to all intersecting boundary lines passing through each of the intersections to be identified, wherein the intersection attribute information of each of the intersections to be identified is specifically the first attribute matrix of each of the intersections to be identified; A set of first attribute matrices of the at least one intersection to be identified is acquired to obtain a first attribute matrix set, wherein the intersection attribute information of the at least one intersection to be identified is specifically the first attribute matrix set.

6. The method according to claim 5, characterized in that Also includes: Based on the element value in the triplet corresponding to one of the intersecting boundary lines, proportional conversion is performed on the corresponding element values ​​in other triplet groups in the first attribute matrix set.

7. The method according to claim 5, characterized in that The acquiring intersection attribute information of at least one reference intersection in the second image includes: Acquire a second attribute matrix set of at least one reference intersection in the second image stored in a database; wherein the second attribute matrix set is a set of second attribute matrices of the at least one reference intersection; the second attribute matrix is ​​constructed by a triple consisting of the number of intersecting boundary lines passing through the reference intersection, the deflection angle of each intersecting boundary line passing through the reference intersection in a predetermined rotation direction and a predetermined coordinate axis direction in the preset plane coordinate system, and the straight-line distance between the reference intersection and the nearest intersection point on each intersecting boundary line passing through the reference intersection; Alternatively, the acquiring intersection attribute information of at least one reference intersection in the second image includes: Obtaining the number of intersections of the at least one reference intersection; For each of the reference intersection points, respectively, the number of intersecting boundary lines passing through the reference intersection point is obtained; based on a preset plane coordinate system, the deflection angle of each intersecting boundary line passing through the reference intersection point in a predetermined rotation direction and a direction of one of the predetermined coordinate axes in the preset plane coordinate system is obtained; the straight-line distance between the reference intersection point and the nearest intersection point on each intersecting boundary line passing through the reference intersection point is obtained; For each intersecting boundary line passing through the reference intersection, respectively, a triplet consisting of the number of boundary lines passing through the reference intersection, the deflection angle of each intersecting boundary line passing through the reference intersection with one of the predetermined coordinate axis directions in the preset plane coordinate system according to a predetermined rotation direction, and the straight-line distance between the reference intersection and the nearest intersection point on each intersecting boundary line passing through the reference intersection; A second attribute matrix of the reference intersection is constructed based on the triplets corresponding to all intersecting boundary lines passing through the reference intersection, wherein the intersection attribute information of the reference intersection is specifically the second attribute matrix of the reference intersection; A set of second attribute matrices of the at least one reference intersection is acquired to obtain a second attribute matrix set, wherein the intersection attribute information of the at least one reference intersection is specifically the second attribute matrix set.

8. The method according to claim 7, characterized in that The element value in each triplet in the second attribute matrix set is the element value that is proportionally converted based on the element value in the triplet corresponding to one of the intersecting boundary lines passing through the reference intersection point.

9. The method according to claim 1, characterized in that: The step of obtaining a similarity matrix of the first attribute matrix of the at least one intersection to be identified from the second attribute matrix set includes: Selecting the first attribute matrix with the largest dimension in the first attribute matrix set; Taking the dimension of the first attribute matrix with the largest dimension as the first dimension, obtaining a second attribute matrix with the dimension of the first dimension from the second attribute matrix set, and obtaining at least one first target second attribute matrix; Respectively obtaining difference matrices between the first attribute matrix with the largest dimension and each second attribute matrix in the at least one first target second attribute matrix to obtain a first difference matrix set; Taking the to-be-identified intersection corresponding to the first attribute matrix with the largest dimension as the first to-be-identified intersection, obtaining a second to-be-identified intersection among the at least one to-be-identified intersection, which is connected to the first to-be-identified intersection by a boundary line and whose corresponding first attribute matrix has the largest dimension in the first attribute matrix set; Taking the dimension of the first attribute matrix of the second intersection to be identified as the second dimension, obtaining a second attribute matrix with the dimension of the second dimension from the second attribute matrix set, and obtaining at least one candidate second attribute matrix; Taking the reference intersection corresponding to the at least one first target second attribute matrix as the first reference intersection, taking the reference intersection corresponding to the at least one candidate second attribute matrix as the second reference intersection, selecting a candidate second attribute matrix of a second reference intersection having a boundary line connection relationship with the first reference intersection from the at least one candidate second attribute matrix, to obtain at least one second target second attribute matrix; Respectively obtaining difference matrices between the first attribute matrix of the second intersection to be identified and each second attribute matrix in the at least one second target attribute matrix to obtain a second difference matrix set; Taking the second intersection to be identified as the first intersection to be identified, start iteratively executing the operation of obtaining a second intersection to be identified among the at least one intersection to be identified, which is connected to the first intersection to be identified by a boundary line and whose corresponding first attribute matrix has the largest dimension in the first attribute matrix set, until no second intersection to be identified among the at least one intersection to be identified, which is connected to the first intersection to be identified by a boundary line and whose corresponding first attribute matrix has the largest dimension in the first attribute matrix set, is obtained, and all second difference matrix sets are obtained.

10. The method according to claim 9, characterized in that The determining the similarity between the first attribute matrix set and the second attribute matrix set based on the number of similarity matrices of the first attribute matrix of the at least one intersection to be identified and the similarity between the first attribute matrix of the at least one intersection to be identified and the corresponding similarity matrix comprises: Taking the first intersection to be identified and each of the obtained second intersections to be identified as target intersections to be identified, and determining the similarity of the target intersections to be identified based on the skew angles and straight-line distances of each dimension in the first attribute matrix of the target intersections to be identified, and the weights of the first difference matrix set or the second difference matrix set corresponding to the target intersections to be identified; A similarity matrix is ​​constructed based on the number and similarity of the second attribute matrices in the at least one first target second attribute matrix corresponding to the first intersection to be identified, and the number and similarity of the second attribute matrices in each of the second intersections to be identified and each of the second target second attribute matrices corresponding to the second intersections to be identified; Based on the number of second attribute matrices in each dimension in the similarity matrix, a weighted average is performed on the similarity of each dimension to obtain the similarity between the first attribute matrix set and the second attribute matrix set.

11. The method according to any one of claims 1 to 4, characterized in that: Before obtaining the intersection points between the boundary lines in the first image, the method further includes: receiving an image upload request, where the image upload request is used to request uploading of the first image; After determining the degree of overlap between the first image and the second image, the method further includes: If the degree of overlap between the first image and the second image is lower than a preset threshold, the first image and the intersection attribute information of the at least one intersection to be identified are stored in a database.

12. An electronic device, characterized in that: The electronic device comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 11.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 11 is implemented.

Images