WO2022088390A1 - Procédé et appareil de regroupement incrémentiel d'images, dispositif électronique, support de stockage et produit-programme - Google Patents

Procédé et appareil de regroupement incrémentiel d'images, dispositif électronique, support de stockage et produit-programme Download PDF

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WO2022088390A1
WO2022088390A1 PCT/CN2020/134074 CN2020134074W WO2022088390A1 WO 2022088390 A1 WO2022088390 A1 WO 2022088390A1 CN 2020134074 W CN2020134074 W CN 2020134074W WO 2022088390 A1 WO2022088390 A1 WO 2022088390A1
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cluster
clusters
image data
center
similarity
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PCT/CN2020/134074
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English (en)
Chinese (zh)
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刘凯鉴
余世杰
陈浩彬
陈大鹏
赵瑞
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浙江商汤科技开发有限公司
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Priority to KR1020227013791A priority Critical patent/KR20220070482A/ko
Priority to JP2022524182A priority patent/JP2023502863A/ja
Publication of WO2022088390A1 publication Critical patent/WO2022088390A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
    • G06V10/763—Non-hierarchical techniques, e.g. based on statistics of modelling distributions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting

Definitions

  • the present disclosure is based on a Chinese patent application with an application number of 202011185911.8 and an application date of October 30, 2020, and claims the priority of the Chinese patent application, the entire contents of which are hereby incorporated by reference into the present disclosure.
  • the embodiments of the present disclosure relate to the technical field of computer vision, and in particular, to a method and apparatus for incremental clustering of images, an electronic device, a storage medium, and a program product.
  • the present disclosure provides an incremental clustering method, device, electronic device, storage medium and program product for images, which are beneficial to solve the problem that the clustering effect is affected by the drift of the clustering center in the incremental clustering .
  • a first aspect of the embodiments of the present disclosure provides an incremental clustering method for images, the method comprising:
  • a cluster center the M is an integer greater than or equal to 1; a second image data set is obtained, and the first cluster center is used to combine the second image data set and the first cluster.
  • the first cluster includes a first cluster A, a first cluster B, and a first cluster C;
  • the cluster center merges the second image data set with the first cluster, including:
  • the second image data set includes a plurality of image data
  • cluster the plurality of image data to obtain isolated image data and a second cluster
  • Merging the isolated image data with the first cluster A and merging the second cluster with the first cluster B using the first cluster center; in the second image data
  • the single image data is merged with the first cluster C by using the first cluster center.
  • the plurality of image data in the second image data set is clustered, and the isolated image data and the second cluster are obtained by using the obtained isolated image data and the first cluster A and the first cluster included in the first cluster respectively.
  • the cluster can absorb a single sample and merge between the clusters.
  • the first cluster has a corresponding second cluster center; when using the first cluster center to combine the second image data set with the Before the first cluster is merged, the method further includes:
  • K first clusters are determined from the first clusters by using the second cluster centers.
  • the second cluster has a corresponding third cluster center; the second cluster center is determined from the first cluster by using the second cluster center Get K first clusters, including:
  • the first cluster is screened, which is beneficial to determine the image in the second image data set.
  • the first cluster with more similar data cluster categories.
  • using the first cluster center to combine the isolated image data with the first cluster A includes:
  • the first cluster center D is each of the first clusters in the K first clusters the first cluster center corresponding to the first sub-cluster; for each of the K first clusters, determine the fourth similarity in each of the first clusters
  • the first number of the first cluster centers D whose degree is greater than the first threshold; the first cluster with the largest first number among the K first clusters is determined as the first cluster Cluster A; merge the isolated image data with the first cluster cluster A.
  • using the first cluster center to merge the second cluster with the first cluster B includes:
  • the second cluster Divide the second cluster into N second subclusters, and obtain a fourth cluster center corresponding to each second subcluster in the N second subclusters; the N is greater than or equal to 1 obtain the fifth similarity between the fourth cluster center and the first cluster center E; the first cluster center E is each of the K first clusters The first cluster center corresponding to each first sub-cluster of ; for each first cluster in the K first clusters, determine the The second number of the first cluster centers E whose fifth similarity is greater than the second threshold; the first cluster with the largest second number among the K first clusters is determined as the first cluster a cluster B; the second cluster is merged with the first cluster B.
  • the first cluster B has at most first clusters that are closer to the second subcluster of the second cluster. sub-cluster, merging the second cluster into the first cluster B can make the clustering result more accurate.
  • the use of the first cluster center to combine the single image data with the first cluster C includes:
  • the first cluster center F is each of the first clusters in the K first clusters the first cluster center corresponding to the first sub-cluster; for each of the K first clusters, determine the sixth similarity in each of the first clusters
  • the third number of the first cluster centers F whose degree is greater than the third threshold; the first cluster with the largest third number among the K first clusters is determined as the first cluster Cluster C; merge the single image data with the first cluster cluster C.
  • the M is less than or equal to a fourth threshold; when using the first cluster center to combine the second image data set with the first cluster Afterwards, the method further includes:
  • the first cluster center is updated; when the R is greater than the fourth threshold, obtain the fourth quantity of image data in each of the R third sub-clusters; according to the Sorting the R third subclusters from large to small with the fourth number to obtain a fourth clustering cluster sequence, selecting the first P third subclusters in the fourth clustering cluster sequence, and using the P third subclusters.
  • the fifth cluster center corresponding to the three sub-clusters updates the first cluster center; the P is less than or equal to the fourth threshold.
  • the first cluster is obtained by clustering the image data in the first image data set; the first cluster is divided into M first subclusters, including:
  • the first cluster can be divided into the M first sub-clusters by using the similarity matrix.
  • the dividing the first cluster into the M first sub-clusters based on the similarity matrix includes:
  • the plurality of vertices with the seventh similarity greater than the fifth threshold can be divided into a first sub-cluster by using the connectivity graph.
  • a second aspect of the embodiments of the present disclosure provides an apparatus for incremental clustering of images, and the apparatus includes:
  • a first obtaining module configured to obtain a first cluster of a first image data set
  • a first segmentation module configured to divide the first cluster into M first sub-clusters, and obtain the M first sub-clusters the first cluster center corresponding to each first sub-cluster in the first sub-cluster; the M is an integer greater than or equal to 1
  • the merging module is configured to obtain a second image data set, using the first cluster center The second image dataset is merged with the first cluster.
  • a third aspect of the embodiments of the present disclosure provides an electronic device, the electronic device includes an input device and an output device, and further includes a processor adapted to implement one or more instructions; and a computer storage medium, the computer storage medium storing There is one or more instructions adapted to be loaded by the processor and to perform the steps in any of the embodiments of the first aspect above.
  • a fourth aspect of the embodiments of the present disclosure provides a computer storage medium, where the computer storage medium stores one or more instructions, and the one or more instructions are suitable for being loaded by a processor and executing any one of the foregoing first aspects steps in the implementation.
  • a fifth aspect of the embodiments of the present disclosure provides a computer program product, the computer program product includes one or more instructions, and the one or more instructions are suitable for being loaded by a processor and executing any one of the implementations of the first aspect above steps in the method.
  • the embodiment of the present disclosure obtains the first cluster of the first image data set; divides the first cluster into M first sub-clusters, and obtains the M first sub-clusters.
  • the first cluster is divided into a plurality of first sub-clusters, and the second image data set is merged by the first cluster based on the first cluster center of the first sub-cluster.
  • the cluster center (the cluster center of the first cluster, that is, the main center) will be affected by the new image data and cause drift, which is beneficial to Make the clustering results more accurate to improve the clustering effect.
  • the second image data set does not need to perform similarity calculation with the first image data set as a whole, which is beneficial to reduce the computational complexity.
  • FIG. 1 is a schematic diagram of an application environment provided by an embodiment of the present disclosure
  • FIG. 2 is a schematic flowchart of a method for incremental clustering of images according to an embodiment of the present disclosure
  • 3A is a schematic diagram of a connectivity graph of a first cluster according to an embodiment of the present disclosure
  • 3B is a schematic diagram of dividing a first cluster into first sub-clusters according to an embodiment of the present disclosure
  • FIG. 4A is a schematic diagram of a clustering result of a second image data set according to an embodiment of the present disclosure
  • 4B is a schematic diagram of merging isolated image data with a first cluster according to an embodiment of the present disclosure
  • 4C is a schematic diagram of merging a second cluster with a first cluster according to an embodiment of the present disclosure
  • FIG. 5 is a schematic flowchart of updating a first cluster center according to an embodiment of the present disclosure
  • FIG. 6 is a schematic flowchart of another method for incremental clustering of images according to an embodiment of the present disclosure
  • FIG. 7 is a schematic structural diagram of an apparatus for incremental clustering of images according to an embodiment of the present disclosure.
  • FIG. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
  • An embodiment of the present disclosure proposes an incremental clustering method for image data, which can be implemented based on the application environment shown in FIG. 1 .
  • the application environment mainly includes an image processing center 101 and an image acquisition device 102 .
  • the processing center 101 includes but is not limited to a server 1011, a terminal and a database.
  • the image acquisition device 102 may be a camera or a camera deployed in scenes such as gate passages, shopping malls, and residential areas, and is used to collect images, such as face images, video surveillance images, and the image processing center 101 may be The monitoring center, the image processing center 101 can introduce a video cloud node (Video Cloud Node, VCN) 1012 to manage the video monitoring, for example: display the images on the display 1013, and store the images in the database 1014 after clustering.
  • VCN Video Cloud Node
  • the image collection device 102 may also be a user terminal, and the images it collects may be photos taken by the user, for example, photos posted by the user on social media, and the image processing center may be the processing background of social media.
  • the image acquisition device 102 can upload the collected images to the image processing center 101, and the image processing center 101 performs operations such as feature extraction, cluster classification, face recognition, etc. Since the images on the image acquisition device side are generated incrementally every day , and incremental clustering needs to maintain some clusters. With the continuous increase of image data and the continuous progress of incremental clustering, the cluster center of the original maintained cluster will have the risk of drift, which makes the clustering The effect gradually deteriorates, so the server 1011 can be used to execute the incremental clustering method proposed by the embodiment of the present disclosure, so as to solve the problem that the clustering effect is affected by the drift of the cluster center in the incremental clustering.
  • the above-mentioned server 1011 may be an independent physical server, a server cluster or a distributed system, or may provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, and middleware services , domain name services, security services, and cloud servers for basic cloud computing services such as big data and artificial intelligence platforms.
  • FIG. 2 is a schematic flowchart of an image incremental clustering method provided by an embodiment of the present disclosure.
  • the image incremental clustering method is applied to a server, as shown in FIG. 2 , including steps S21 to S23:
  • the first image dataset refers to an image dataset that has been clustered into multiple clusters before the current batch of image data. ) is the current batch of data, then the data of the face image that has been uploaded to the server before this is the first image data set.
  • the first cluster is a cluster obtained by clustering the image data in the first image data set, and the clustering algorithm used may be a K-means clustering algorithm. It should be understood that each cluster exists The corresponding cluster center, that is, the second cluster center.
  • S22 Divide the first cluster into M first subclusters, and obtain a first cluster center corresponding to each first subcluster in the M first subclusters; the M is greater than or An integer equal to 1.
  • FIG. 3A is a schematic diagram of a connectivity graph of a first cluster according to an embodiment of the present disclosure.
  • the connectivity graph of the first cluster includes a first cluster 301 and a second cluster center 302 , wherein the first clustering cluster 301 is a clustering cluster obtained by clustering the image data in the first image data set; the second clustering center 302 is that each clustering cluster has a corresponding clustering center.
  • FIG. 3B is a schematic diagram of dividing a first cluster into first sub-clusters according to an embodiment of the present disclosure.
  • the division of the first cluster into first sub-clusters includes a first cluster 301 , the second cluster center 302, the first sub-cluster 303 and the first cluster center 304, wherein the first sub-cluster 303 is a sub-cluster obtained by dividing the first cluster cluster 301; the first cluster center 304 is the cluster center of each first subcluster.
  • the first sub-cluster is the sub-cluster obtained by dividing the first cluster. For each first cluster in the first data set, the similarity between the image data in the first cluster is obtained, that is, the first sub-cluster is obtained. Seven degrees of similarity, get a similarity matrix, and then obtain a connected graph with the image data in the first cluster as vertices, as shown in Figure 3A, for every two vertices in the connected graph, query from the similarity matrix Its similarity, in the case of clustering the first image data set, the threshold used is X, that is, the fifth threshold, then the multiple image data whose similarity is greater than this X is divided into a more compact first sub-cluster , so that M first subclusters are obtained. As shown in FIG.
  • the first cluster shown in FIG. 3A is divided into M first subclusters through the analysis of the connected graph.
  • the cluster center of each first sub-cluster in the M first sub-clusters is obtained, that is, the first cluster center, then each first cluster cluster can be composed of a main cluster Center and M sub-cluster center descriptions. Describing the first cluster with a more compact sub-cluster is beneficial to solve the problem that the expression ability of a single main cluster center is weakened with the incorporation of new image data.
  • S23 Acquire a second image data set, and combine the second image data set with the first cluster by using the first cluster center.
  • FIG. 4A is a schematic diagram of a clustering result of a second image dataset provided by an embodiment of the present disclosure.
  • the clustering result of the second image dataset includes a second image dataset 401 , a second cluster Cluster 402, isolated image data 403 and third cluster center 404, wherein the second image data set 401 is the data set of the current batch of images uploaded by the image acquisition device; The image data is clustered by clustering; the isolated image data 403 is the isolated image data that has not been clustered; the third cluster center 404 is the cluster center where each second cluster exists.
  • FIG. 4B is a schematic diagram of merging isolated image data with a first cluster according to an embodiment of the present disclosure. As shown in FIG. 4B , merging isolated image data with a first cluster includes a first cluster A 405 and an isolated cluster A 405 . Image data 403, wherein the first cluster A 405 is the first cluster A determined in the first cluster.
  • FIG. 4C is a schematic diagram of merging a second cluster with a first cluster according to an embodiment of the present disclosure. As shown in FIG. 4C , the combination of the second cluster and the first cluster includes the first cluster B 406 and the second cluster 407, wherein the first cluster B 406 and the second cluster 407 belong to the same cluster category.
  • the second image data set is the data set of the current batch of images uploaded by the image acquisition device, and is obtained from the images uploaded by the image acquisition device.
  • the first cluster includes a first cluster A, a first cluster B, and a first cluster C, and when the second image data set includes multiple image data, cluster to get the clustering result.
  • the clustering result includes unclustered isolated image data and several second clusters, and each of the several second clusters has a corresponding cluster center, that is, the third cluster center, see Figure 4A.
  • the first cluster A is determined from the first cluster, and the first cluster center is used to merge it with the first cluster A, that is, as shown in FIG.
  • the isolated image data Absorbed into the first cluster A, the first cluster A and the isolated image data belong to the same cluster category.
  • For each second cluster determine the first cluster B from the first cluster, and use the first cluster center to merge it with the first cluster B, that is, as shown in FIG. 4C .
  • the first cluster B and the second cluster belong to the same cluster category. Similar to the isolated image data, in the case where there is only a single image data in the second image data set, that is, the newly added image data is only a single image data, and there is no need to perform a clustering operation on the second image data set.
  • the first cluster C is determined, and the first cluster C is merged with the first cluster C by using the first cluster center, and the first cluster C and the single image data belong to the same cluster category.
  • the method before using the first cluster center to combine the second image data set with the first cluster, the method further includes:
  • K first clusters are determined from the first clusters by using the second cluster centers.
  • all the first clusters need to be preliminarily screened by using the second cluster center of the first cluster, and from all the first clusters K first clusters are determined, and then the above-mentioned first cluster A and first cluster B, or first cluster C are selected from the K clusters.
  • the K first clusters may be the top K after sorting all the first clusters by using the second cluster center, for example: the top 20 of the 100 first clusters after sorting
  • the K first clusters may also be all sorted first clusters, for example, 100 first clusters are still selected after sorting.
  • Using the second cluster center to preliminarily screen the first cluster is beneficial to determine the first cluster that is more similar to the image data clustering category in the second image data set, such as the above-mentioned first cluster A, the first cluster B and the first cluster C.
  • the determining K first clusters from the first clusters by using the second cluster center includes:
  • the second image data set when the second image data set is clustered to obtain isolated image data and multiple second clusters, for the isolated sample image data, calculate the difference between it and the second cluster center of each first cluster.
  • the second cluster calculate the second similarity between the corresponding third cluster center and the second cluster center of each first cluster, respectively according to the first similarity Sort all the first clusters from high to low degree and the second similarity to obtain the corresponding first and second cluster sequences, and then from the first and second clusters
  • the first K first cluster clusters are respectively selected from the cluster sequence.
  • the third similarity between the single image data and the second cluster center of each first cluster is calculated, and the third similarity is from high to low.
  • the first clusters are sorted to obtain a corresponding third cluster sequence, and then the top K first clusters are selected from the third cluster sequence.
  • the using the first cluster center to combine the isolated image data with the first cluster A includes:
  • the first cluster center D is each of the first clusters in the K first clusters the first cluster center corresponding to the first sub-cluster; for each of the K first clusters, determine the fourth similarity in each of the first clusters
  • the first number of the first cluster centers D whose degree is greater than the first threshold; the first cluster with the largest first number among the K first clusters is determined as the first cluster Cluster A; merge the isolated image data with the first cluster cluster A.
  • the first cluster A needs to be determined from the first K first clusters selected. It should be noted that the first K first clusters may be sorted All the first clusters of . First, the similarity between the isolated image data and the cluster center (ie, the first cluster center D) of each first sub-cluster of each first cluster in the K first clusters is calculated, and is determined as the first cluster center D.
  • the K first clusters Four similarity degrees, and then analyze the K first clusters to determine the number of first cluster centers D in each first cluster that satisfy the fourth similarity greater than the first threshold, and determine it as the first number, Determine the first cluster with the largest first number as the first cluster A, for example, among the K first clusters, the first cluster 1 has 20 such first cluster centers D, The first cluster 2 has 18 such first cluster centers D, ..., the first cluster K has 15 such first cluster centers D, and the first cluster 1 has the largest number, then it is It is determined to be the first cluster A, that is to say, the first sub-cluster A that is most similar to the isolated image data exists in the first cluster A. Merging the isolated image data into the first cluster A can make the clustering The results are more accurate.
  • the merging the second cluster cluster and the first cluster cluster B by using the first cluster center includes:
  • the second cluster Divide the second cluster into N second subclusters, and obtain a fourth cluster center corresponding to each second subcluster in the N second subclusters; the N is greater than or equal to 1 obtain the fifth similarity between the fourth cluster center and the first cluster center E; the first cluster center E is each of the K first clusters The first cluster center corresponding to each first sub-cluster of ; for each first cluster in the K first clusters, determine the The second number of the first cluster centers E whose fifth similarity is greater than the second threshold; the first cluster with the largest second number among the K first clusters is determined as the first cluster a cluster B; the second cluster is merged with the first cluster B.
  • the first cluster B needs to be determined from the first K first clusters selected.
  • the clusters can be all the first cluster clusters after sorting. First, divide each second cluster into N second sub-clusters according to the method of dividing the first cluster, and calculate the cluster center of each second sub-cluster, that is, the fourth cluster center, and then calculate The similarity between the fourth cluster center and the cluster center (ie, the first cluster center E) of each first sub-cluster of each first cluster in the K first clusters is determined as eh
  • the fifth similarity and then analyze the K first clusters to determine the number of first cluster centers E that satisfy the fifth similarity greater than the second threshold in each first cluster, and determine it as the second number , determine the first cluster with the second largest number as the first cluster B, for example: among the K first clusters, the first cluster 1 has 30 such first cluster centers E , the first cluster 2 has 15 such first cluster centers E, ..., the first cluster K has
  • the combining the single image data with the first cluster C by using the first cluster center includes:
  • the first cluster center F is each of the first clusters in the K first clusters the first cluster center corresponding to the first sub-cluster; for each of the K first clusters, determine the sixth similarity in each of the first clusters
  • the third number of the first cluster centers F whose degree is greater than the third threshold; the first cluster with the largest third number among the K first clusters is determined as the first cluster Cluster C; merge the single image data with the first cluster cluster C.
  • the first cluster C For the merging of single image data, it is necessary to determine the first cluster C from the selected top K first clusters. It should be noted that the top K first clusters may be sorted All first cluster clusters. First, the similarity between the single image data and the cluster center (ie, the first cluster center F) of each first sub-cluster of each first cluster in the K first clusters is calculated, and it is determined as the first cluster center.
  • the cluster center ie, the first cluster center F
  • the M is less than or equal to a fourth threshold; after the second image data set and the first cluster are merged by using the first cluster center, as shown in FIG. 5 As shown, the method further includes:
  • the merged first cluster is divided into R third sub-clusters according to the method of dividing the first cluster, and the fifth cluster center of each third sub-cluster is calculated, and the third sub-cluster is determined according to R.
  • the number of three sub-clusters if the number of third sub-clusters is less than or equal to the fourth threshold, for example: 20, the R third sub-clusters are reserved, and the fifth cluster center of these R third sub-clusters is used as The new sub-center of the merged first cluster to update the original first cluster center, then the merged first cluster is described by the second cluster center and the R fifth cluster centers .
  • the fourth threshold for example: 20
  • the R third sub-clusters are reserved, and the fifth cluster center of these R third sub-clusters is used as The new sub-center of the merged first cluster to update the original first cluster center, then the merged first cluster is described by the second cluster center and the R fifth cluster centers .
  • the R third sub-clusters are sorted according to the number of image data in each third sub-cluster (that is, the fourth number) from large to small to obtain the fourth cluster.
  • Cluster-like sequence select the first P third sub-clusters to keep, for example: only keep the first 20 third sub-clusters, discard the rest of the third sub-clusters, and use the fifth cluster center of the P third sub-clusters as the merge Then, the merged first cluster is described by using the second cluster center and the P fifth cluster centers. It should be understood that each time a cluster is divided into sub-clusters, only a preset number of sub-clusters are reserved.
  • both M and N are less than or equal to the fourth threshold, so that when there are many sub-clusters.
  • the embodiment of the present disclosure obtains the first cluster of the first image data set; divides the first cluster into M first sub-clusters, and obtains the M first sub-clusters.
  • the first cluster is divided into a plurality of first sub-clusters, and the second image data set is merged by the first cluster based on the first cluster center of the first sub-cluster.
  • the cluster center (the cluster center of the first cluster, that is, the main center) will be affected by the new image data and cause drift, which is beneficial to Make the clustering results more accurate to improve the clustering effect.
  • the second image data set does not need to perform similarity calculation with the first image data set as a whole, which is beneficial to reduce the computational complexity.
  • FIG. 6 is a schematic flowchart of another image incremental clustering method provided by an embodiment of the present disclosure, as shown in FIG. 6, including steps S61 to S66:
  • S62 Divide the first cluster into M first subclusters, and obtain a first cluster center corresponding to each first subcluster in the M first subclusters; the M is greater than or an integer equal to 1;
  • the incremental clustering method needs to maintain some clusters in the clustering process.
  • the traditional clustering algorithm uses a single cluster center to describe a cluster, such as taking the mean of all sample features in the cluster to obtain the cluster center , but different clusters have different degrees of sparseness, so simply adopting a single cluster center with mean value is easy to lose the rich sample information inside the cluster. As the process of incremental clustering continues, the clustering effect will be gradually affected. .
  • the similarity matrix S can be obtained. Assuming that the threshold used for clustering is ⁇ , a higher threshold ⁇ ' needs to be set, that is, ⁇ '> ⁇ is satisfied to cluster a cluster. The cluster is split into several tighter subclusters.
  • Clusters can be analyzed using methods based on connectivity graph analysis to obtain the polycentricity of clusters.
  • the similarity matrix is calculated for the clusters.
  • a cluster can be divided into several more compact sub-clusters, so that multiple sub-cluster centers can be obtained, plus as The center of the cluster in the main center constitutes the multi-center description of the cluster.
  • using the design analysis of cluster multi-center based on connected graph analysis to obtain multiple sub-centers includes: first, for each cluster, by setting a higher threshold (needs to be higher than the clustering threshold), the cluster is Scatter into several more compact connected sub-graphs, and calculate the sub-centers for each connected sub-graph, so that multiple sub-centers can be obtained.
  • step S69 generating a number of clusters and unclustered isolated samples, and obtaining the existing clustering results in step S67 for cluster merging.
  • Multi-center incremental clustering method based on a single main center and multiple sub-centers On the basis of obtaining the main center and multiple sub-centers, in the process of incremental clustering, first use the main center and new data to perform a TopK search. sieve, and then further determine whether to absorb new samples or other clusters based on multiple sub-centers.
  • the process of cluster merging involves merging between clusters and absorbing individual isolated samples into clusters. For the absorption of isolated sample points, based on the multi-center design, a lower threshold is first set, and the main center is used to search for TopK, and then according to whether the sub-center and the sample point meet the clustering threshold ⁇ . In this case, there may be multiple clusters and isolated sample points to meet such requirements, and the cluster with the largest number of sub-centers that meet the requirements is used as the target cluster.
  • a lower threshold is also used to filter and retrieve TopK, and then according to whether there are sub-center pairs between clusters that meet the threshold requirements, when there are multiple clusters that meet the requirements, take the threshold that meets the requirements.
  • the cluster with the largest number of sub-centers is used as the target cluster.
  • a single main center and multiple sub-centers in the multi-center mechanism are comprehensively utilized.
  • the main center is used to participate in the calculation of similarity, and then through multiple sub-centers and pending
  • the similarity of a single sample or cluster of clusters is calculated to further determine whether the absorption of a single sample or the merging of clusters is completed.
  • This architecture comprehensively utilizes the advantages of multi-center representation, which can improve the clustering effect without increasing too much computational complexity.
  • each sub-center can be sorted from large to small according to the number of sample points represented, for example, only the first 20 sub-centers are taken at most.
  • a multi-center construction method of face clusters is proposed, which can be used to obtain the description of a single main center and multiple sub-centers of a face cluster. It solves the problem that the description of a cluster is to maintain a cluster center, ignoring some compact sub-cluster information inside the cluster, and as the data continues to increase, due to the maintenance of a single cluster center, the cluster center will continue to be subject to new changes.
  • the influence of the samples has a certain risk of center drift, and the influence of the existing samples in the cluster will continue to weaken, reducing the expression ability of the center.
  • a single cluster center will lose the sample information inside the cluster during the incremental clustering process.
  • a single cluster center is usually maintained for each cluster, and data is continuously added.
  • the clustering center is used to calculate the similarity between new samples or clusters to merge and update the clusters, and the clustering center will also be updated continuously.
  • a single multi-center will gradually lose the rich sample information within the cluster, and it is also prone to drift, which will affect the clustering effect over time.
  • an incremental clustering architecture based on multi-center is proposed. Using this architecture, the computational complexity and clustering accuracy of incremental clustering using multi-center representation can be well balanced. The merging of samples and clusters solves the problem that the multi-center setting of the prior art will have a great impact on the computing speed and storage of clustering in large-scale data scenarios.
  • an embodiment of the present disclosure further provides an apparatus for incremental clustering of images. Please refer to FIG. 7 .
  • FIG. 7 provides an image increment according to an embodiment of the present disclosure.
  • a schematic diagram of the structure of the clustering device, as shown in Figure 7, the device includes:
  • the first acquisition module 71 is configured to acquire the first cluster of the first image data set
  • a first segmentation module 72 configured to segment the first cluster into M first sub-clusters, and obtain a first cluster center corresponding to each first sub-cluster in the M first sub-clusters;
  • the M is an integer greater than or equal to 1;
  • the merging module 73 is configured to obtain a second image data set, and use the first cluster center to merge the second image data set and the first cluster.
  • the first cluster includes a first cluster A, a first cluster B and a first cluster C;
  • the merging module 73 is configured to: if the second image data set includes a plurality of image data, cluster the plurality of image data, obtaining isolated image data and a second cluster; using the first cluster center to merge the isolated image data with the first cluster A; and using the first cluster center to combine the first cluster The two clusters are merged with the first cluster B; in the case that there is only a single image data in the second image data set, the first cluster center is used to combine the single image data with the second image data A cluster C is merged.
  • the first cluster has a corresponding second cluster center; when using the first cluster center to associate the second image data set with the first cluster Before merging, the merging module 73 is further configured to: determine K first clusters from the first clusters by using the second cluster center.
  • the second cluster has a corresponding third cluster center; after using the second cluster center to determine K first clusters from the first cluster
  • the merging module 73 is configured to: obtain a first similarity between the isolated image data and the second cluster center; Sorting the clusters to obtain a first cluster sequence, and selecting the top K first clusters in the first cluster sequence; and obtaining the distance between the third cluster center and the second cluster center the second similarity; sort the first clusters according to the second similarity from high to low to obtain a second cluster sequence, and select the top K first clusters in the second cluster sequence Clustering; or, obtaining a third similarity between the single image data and the second cluster center; sorting the first clusters according to the third similarity from high to low to obtain For the third cluster sequence, the top K first clusters in the third cluster sequence are selected.
  • the merging module 73 is configured to: obtain the isolated image data and the first cluster A.
  • a fourth similarity between cluster centers D; the first cluster center D is the The first cluster center; for each first cluster in the K first clusters, determine that the fourth similarity in each first cluster is greater than the first threshold.
  • the first number of the first cluster centers D; the first cluster with the largest number of the K first clusters is determined as the first cluster A; the isolated image The data is merged with the first cluster A.
  • the merging module 73 is configured to: combine the second cluster The cluster is divided into N second subclusters, and the fourth cluster center corresponding to each second subcluster in the N second subclusters is obtained; the N is an integer greater than or equal to 1; obtain the The fifth similarity between the fourth cluster center and the first cluster center E; the first cluster center E is each first child of each first cluster in the K first clusters the first cluster center corresponding to the cluster; for each first cluster in the K first clusters, determine that the fifth similarity in each first cluster is greater than the first cluster
  • the second number of the first cluster centers E with two thresholds; the first cluster with the largest second number among the K first clusters is determined as the first cluster B; The second cluster is merged with the first cluster B.
  • the merging module 73 is configured to: obtain the single image data and the first cluster C. a sixth degree of similarity between cluster centers F; the first cluster center F is the The first cluster center; for each first cluster in the K first clusters, determine the sixth similarity greater than the third threshold in each first cluster. the third number of the first cluster centers F; the first cluster with the third largest number of the K first clusters is determined as the first cluster C; the single image The data is merged with the first cluster C.
  • the M is less than or equal to a fourth threshold; the first dividing module 72 is further configured to: divide the merged first cluster into R third sub-clusters, and obtain the The fifth cluster center of each third sub-cluster in the R third sub-clusters; the R is an integer greater than or equal to 1; when the R is less than or equal to the fourth threshold, keep the R third sub-clusters, and the first cluster center is updated with the fifth cluster center corresponding to the R third sub-clusters; when the R is greater than the fourth threshold, Obtain the fourth quantity of image data in each of the R third sub-clusters; sort the R third sub-clusters according to the fourth quantity in descending order to obtain a fourth cluster Cluster-like sequence, select the first P third subclusters in the fourth clustering cluster sequence, and use the fifth clustering centers corresponding to the P third subclusters to update the first clustering center ; the P is less than or equal to the fourth threshold.
  • the first dividing module 72 is configured to: acquire between the image data in the first cluster The seventh similarity is obtained, and a similarity matrix is obtained; the first cluster is divided into the M first sub-clusters based on the similarity matrix.
  • the first dividing module 72 is configured to: obtain the first sub-cluster with the first sub-cluster
  • the image data in the cluster is a connected graph composed of vertices; the seventh similarity between the vertices in the connected graph is obtained by querying the similarity matrix; the seventh similarity is greater than the fifth similarity
  • the multiple vertices of the threshold are divided into a first sub-cluster to obtain the M first sub-clusters.
  • each unit in the apparatus for incremental clustering of images shown in FIG. 7 may be respectively or all merged into one or several other units to form, or some of the unit(s) may be further It can be further divided into multiple units with smaller functions, which can realize the same operation without affecting the realization of the technical effects of the embodiments of the present disclosure.
  • the above-mentioned units are divided based on logical functions. In practical applications, the function of one unit may also be implemented by multiple units, or the functions of multiple units may be implemented by one unit. In other embodiments of the present disclosure, the image-based incremental clustering apparatus may also include other units, and in practical applications, these functions may also be implemented with the assistance of other units, and may be implemented by a plurality of units in cooperation.
  • a general-purpose computing device such as a computer
  • a general-purpose computing device such as a computer
  • a general-purpose computing device such as a computer
  • processing elements such as a central processing unit (CPU), random access storage medium (RAM), read-only storage medium (ROM), etc.
  • storage elements such as a central processing unit (CPU), random access storage medium (RAM), read-only storage medium (ROM), etc.
  • Running a computer program capable of executing the steps involved in the corresponding method as shown in FIG. 2 or FIG. 6, to construct the incremental clustering apparatus of the image as shown in FIG. 7, and to realize the present invention.
  • the computer program can be recorded on, for example, a computer-readable recording medium, and loaded in the above-mentioned computing device through the computer-readable recording medium, and executed therein.
  • the embodiments of the present disclosure further provide an electronic device.
  • the electronic device includes at least a processor 81 , an input device 82 , an output device 83 and a computer storage medium 84 .
  • the processor 81 , the input device 82 , the output device 83 and the computer storage medium 84 in the electronic device may be connected through a bus or other means.
  • the computer storage medium 84 may be stored in the memory of the electronic device, the computer storage medium 84 configured to store a computer program including program instructions, the processor 81 configured to execute the program stored by the computer storage medium 84 instruction.
  • the processor 81 (or called CPU (Central Processing Unit, central processing unit)) is the computing core and the control core of the electronic device, which is suitable for implementing one or more instructions, and is suitable for loading and executing one or more instructions to achieve the corresponding Method flow or corresponding function.
  • CPU Central Processing Unit, central processing unit
  • the processor 81 of the electronic device provided by the embodiment of the present disclosure may be configured to perform incremental clustering processing of a series of images:
  • the first cluster includes a first cluster A, a first cluster B and a first cluster C; the processor 81 executes the process of using the first cluster center to Combining the second image data set with the first cluster includes: in the case that the second image data set includes a plurality of image data, clustering the plurality of image data to obtain an isolated image data and a second cluster; combining the isolated image data with the first cluster A using the first cluster center; and combining the second cluster using the first cluster center The cluster is merged with the first cluster cluster B; in the case that there is only a single image data in the second image data set, the single image data is combined with the first cluster by using the first cluster center Cluster C is merged.
  • the first cluster has a corresponding second cluster center; before using the first cluster center to merge the second image data set and the first cluster, The processor 81 is further configured to perform: using the second cluster center to determine K first clusters from the first clusters.
  • the second cluster has a corresponding third cluster center; the processor 81 executes the process of determining the Kth cluster from the first cluster by using the second cluster center.
  • a cluster including: acquiring a first similarity between the isolated image data and the second cluster center; sorting the first clusters according to the first similarity from high to low Obtain the first cluster sequence, and select the top K first clusters in the first cluster sequence; and, obtain the second cluster center between the third cluster center and the second cluster center.
  • the processor 81 performing the process of using the first cluster center to merge the isolated image data with the first cluster A includes: acquiring the isolated image data and the first cluster A.
  • the fourth similarity between centers D; the first cluster center D is the first cluster corresponding to each first sub-cluster of each first cluster in the K first clusters Class center; for each of the K first clusters, determine the first cluster whose fourth similarity is greater than a first threshold in each of the first clusters the first number of cluster centers D; determine the first cluster with the largest first number among the K first clusters as the first cluster A; combine the isolated image data with all The first cluster cluster A is merged.
  • the processor 81 performing the using the first cluster center to merge the second cluster with the first cluster B includes: dividing the second cluster is N second subclusters, and obtains the fourth cluster center corresponding to each second subcluster in the N second subclusters; the N is an integer greater than or equal to 1; obtain the fourth cluster center
  • the processor 81 performing the merging of the single image data and the first cluster C by using the first cluster center includes: acquiring the single image data and the first cluster C.
  • the sixth similarity between centers F; the first cluster center F is the first cluster corresponding to each first sub-cluster of each first cluster in the K first clusters class center; for each first cluster in the K first clusters, determine the first cluster whose sixth similarity is greater than a third threshold in each first cluster the third number of class centers F; determine the first cluster with the largest third number among the K first clusters as the first cluster C; combine the single image data with all The first cluster cluster C is merged.
  • the M is less than or equal to a fourth threshold; after merging the second image data set and the first cluster by using the first cluster center, the processor 81 is further configured to: Execute: divide the merged first cluster into R third subclusters, and obtain the fifth cluster center of each third subcluster in the R third subclusters; the R is greater than or an integer equal to 1; when the R is less than or equal to the fourth threshold, the R third subclusters are reserved, and the fifth cluster center pair corresponding to the R third subclusters is used The first cluster center is updated; when the R is greater than the fourth threshold, obtain the fourth quantity of image data in each of the R third sub-clusters; according to Sorting the R third subclusters in descending order of the fourth number to obtain a fourth clustering cluster sequence, selecting the first P third subclusters in the fourth clustering clustering sequence, and using the P The fifth cluster center corresponding to the third sub-cluster updates the first cluster center; the P is less than or equal to the fourth threshold.
  • the first cluster is obtained by clustering the image data in the first image data set; the processor 81 executes the process of dividing the first cluster into M first clusters. sub-cluster, including: acquiring the seventh similarity between the image data in the first cluster to obtain a similarity matrix; dividing the first cluster into the M based on the similarity matrix first subcluster.
  • the processor 81 performing the dividing the first cluster into the M first sub-clusters based on the similarity matrix includes: obtaining a The image data is a connected graph composed of vertices; the seventh similarity between the vertices in the connected graph is obtained by querying the similarity matrix; the plurality of vertices whose seventh similarity is greater than the fifth threshold are obtained Divide into a first sub-cluster to obtain the M first sub-clusters.
  • the above-mentioned electronic devices may be computers, computer hosts, servers, cloud servers, server clusters, etc.
  • the electronic devices may include, but are not limited to, a processor 81, an input device 82, an output device 83, and a computer storage medium 84.
  • the input device 82 It can be a keyboard, a touch screen, etc.
  • the output device 83 can be a speaker, a display, a radio frequency transmitter, and the like.
  • the schematic diagram may be an example of an electronic device, and does not constitute a limitation on the electronic device, and may include more or less components than the one shown, or combine some components, or different components.
  • the processor 81 of the electronic device implements the steps in the above-mentioned incremental image clustering method when executing the computer program, the above-mentioned embodiments of the incremental image clustering method are all applicable to the electronic device, and can achieve the same or similar beneficial effects.
  • Embodiments of the present disclosure further provide a computer program product, which implements any one of the methods in the foregoing embodiments when the computer program product is executed by a processor.
  • the computer program product can be implemented in hardware, software or a combination thereof.
  • the computer program product is embodied as a computer storage medium, and in other embodiments of the present disclosure, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), etc. Wait.
  • Embodiments of the present disclosure also provide a computer storage medium (Memory), where the computer storage medium is a memory device in an electronic device and is configured to store programs and data.
  • the computer storage medium here may include both a built-in storage medium in the terminal, and certainly also an extended storage medium supported by the terminal.
  • the computer storage medium provides storage space, and the storage space stores the operating system of the terminal.
  • one or more instructions suitable for being loaded and executed by the processor 81 are also stored in the storage space, and these instructions may be one or more computer programs (including program codes).
  • the computer storage medium here may be a high-speed RAM memory, or a non-volatile memory (Non-Volatile Memory), such as at least one disk memory; in some embodiments of the present disclosure, it may also be at least one disk memory.
  • the computer program of the computer storage medium includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, and the like.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.
  • the first cluster is divided into a plurality of first sub-clusters, and the first cluster cluster is combined with the second image data set based on the first cluster center of the first sub-cluster.
  • the first clustering center is used to solve the problem that with the increase of image data, the clustering center will drift due to the influence of the newly added image data, which is conducive to making the clustering result more accurate and improving the clustering effect.

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Abstract

Procédé et appareil de regroupement incrémentiel d'images, dispositif électronique, support de stockage et produit-programme. Ledit procédé consiste : à acquérir une première grappe de regroupement d'un premier ensemble de données d'image (S21) ; à diviser la première grappe de regroupement en M premières sous-grappes, et à acquérir un premier centre de regroupement correspondant à chaque première sous-grappe parmi les M premières sous-grappes, M étant un nombre entier supérieur ou égal à 1 (S22) ; et à acquérir un second ensemble de données d'image, et à combiner le second ensemble de données d'image à la première grappe de regroupement à l'aide du premier centre de regroupement (S23).
PCT/CN2020/134074 2020-10-30 2020-12-04 Procédé et appareil de regroupement incrémentiel d'images, dispositif électronique, support de stockage et produit-programme WO2022088390A1 (fr)

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