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Image clustering method and device and computer readable storage medium

An image clustering and image technology, applied in still image data clustering/classification, computer components, computing, etc., can solve problems such as unstable clustering results, large time overhead, and reduced accuracy and efficiency of image clustering , to achieve the effect of reducing instability and uncertainty, improving efficiency, and improving balance

Pending Publication Date: 2021-11-02
TENCENT TECH (BEIJING) CO LTD
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  • Application Information

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Problems solved by technology

However, the clustering results of the conventional kmeans clustering method are unstable, and it takes a lot of time to process massive data, which reduces the accuracy and efficiency of image clustering

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  • Image clustering method and device and computer readable storage medium
  • Image clustering method and device and computer readable storage medium
  • Image clustering method and device and computer readable storage medium

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Embodiment Construction

[0038] In order to make the purpose, technical solutions and advantages of the application clearer, the application will be further described in detail below in conjunction with the accompanying drawings. All other embodiments obtained under the premise of creative labor belong to the scope of protection of this application.

[0039] In the following description, references to "some embodiments" describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or a different subset of all possible embodiments, and Can be combined with each other without conflict.

[0040] If there is a similar description of "first / second" in the application documents, add the following explanation. In the following description, the terms "first\second\third" are only used to distinguish similar objects, not Represents a specific ordering of objects. It is understandable that "first\second\third" can be exchanged for a specific order or sequenc...

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Abstract

The invention provides an image clustering method and device and a computer readable storage medium, and relates to the field of artificial intelligence. The method comprises the steps of obtaining semantic information of each to-be-processed image in a to-be-processed image set, and performing semantic cluster division on the to-be-processed image set based on the semantic information of each to-be-processed image to obtain n semantic sub-clusters; performing image clustering in each semantic sub-cluster of the n semantic sub-clusters to obtain Ci clustering centers corresponding to each semantic sub-cluster, and further obtaining a clustering center set corresponding to the n semantic sub-clusters; based on the clustering center set, clustering the to-be-processed image set to obtain an image subclass corresponding to each clustering center in the clustering center set; based on the image subclass corresponding to each clustering center, obtaining an image subclass set, wherein the image subclass set comprises an image subclass corresponding to each clustering center, the image subclass set is used for image retrieval. According to the invention, the accuracy and efficiency of image clustering can be improved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, and in particular to an image clustering method, device and computer-readable storage medium. Background technique [0002] Large-scale image retrieval often relies on bucket retrieval. Bucket retrieval mainly divides the original large amount of data into multiple non-overlapping data subsets. It is enough to find matching samples in the bucket, so bucket-based retrieval can improve retrieval efficiency. The currently commonly used bucketing method is generated by kmeans clustering, that is, for 1 million samples, if divided into 10,000 buckets, the cluster center is 10,000. It can be seen that the effect of bucketing has a great impact on the final retrieval results . The ideal bucketing effect is that the similarity in the bucket is high, so that samples with similar characteristics can be assigned to the same bucket, so that the recall of a certain bucket is simila...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/55G06K9/62
CPCG06F16/55G06F18/23213
Inventor 郭卉
Owner TENCENT TECH (BEIJING) CO LTD