Balanced image clustering method based on hierarchical clustering

An image clustering and hierarchical clustering technology, applied in the field of image search, can solve problems such as affecting the average response time of commodity image search engines, reducing query effects, and losing search results, and achieves guaranteed performance, high coincidence, and improved query performance. effect of effect

Inactive Publication Date: 2013-04-17
HANGZHOU TAOTAOSOU TECH
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Problems solved by technology

Therefore, if a cluster contains a much higher than average amount of data, it will seriously affect the average response time of the product image search engine
[0005] 2.

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  • Balanced image clustering method based on hierarchical clustering
  • Balanced image clustering method based on hierarchical clustering
  • Balanced image clustering method based on hierarchical clustering

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[0016] Taking the clustering, indexing, retrieval, and maintenance of clothing product images as an example, the present invention will be described in detail with reference to the accompanying drawings, and the purpose and effect of the present invention will become more obvious.

[0017] Such as figure 1 As shown, the index establishment of the balanced image clustering method based on hierarchical clustering of the present invention includes the following steps:

[0018] Step 1: Perform image feature extraction on the product image, and convert the image data into feature vector data.

[0019] The purpose of feature extraction is to obtain the low-level structure description of the image. Each feature is represented by a d-dimensional vector.

[0020] The present invention uses the global features of the image, that is, each image corresponds to a high-dimensional feature vector. Each dimensional value of the feature vector is used to characterize the characteristics of the image...

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Abstract

The invention discloses a balanced image clustering method based on hierarchical clustering. According to high dimensional feature data of apparel goods images, clusters with balanced sizes are obtained by a method based on hierarchical clustering, and the quantity of data in a single cluster does not exceed a limited threshold value. When in retrieval, after distances among retrieved data and all clustering centers are computed, a plurality of the nearest clusters are selected, and the data are accessed inside the clusters to obtain final research results. Compared with a universal indexing method based on clustering, the method avoids the problem of overlarge accessed data volume when the retrieved data are in large clusters, and ensures research performances. Besides, by accessing a plurality of clusters, the research results and SSA (serial storage architecture) research results have a higher overlap ratio, and research effects are improved.

Description

technical field [0001] The invention relates to the technical field of image search, in particular to a fast approximate k-nearest neighbor retrieval method for high-dimensional image vectors based on hierarchical clustering. Background technique [0002] In content-based image retrieval technology (Content-Based Image Retrieval, CBIR), when a user uploads a product image and expects to search for products that are the same as or similar to the image, the search engine performs feature extraction on the product image uploaded by the user. And select the k images closest to it in the high-dimensional space from the indexed image feature vector database as the result and return. To query the nearest k image features in a large database of indexed features, the most basic method is the SSA method. The SSA method obtains the nearest k images by calculating the distance between the retrieved image and each stored image, and then sorting these distances. This is an exact k-Neare...

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
Inventor 薛亮孙凯
Owner HANGZHOU TAOTAOSOU TECH
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