Computer Science ›› 2021, Vol. 48 ›› Issue (2): 148-152.doi: 10.11896/jsjkx.191200104

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Lightweight Image Retrieval System Based on Feature Clustering

WANG Xiao-fei1, ZHOU Chao2, LIU Li-gang1   

  1. 1 School of Mathematical Sciences,University of Science and Technology of China,Hefei 230000,China
    2 Tencent Computer Systems Co.,Ltd.,Shenzhen,Guangdong 518057,China
  • Received:2019-12-17 Revised:2020-05-18 Online:2021-02-15 Published:2021-02-04
  • About author:WANG Xiao-fei,born in 1995,postgra-duate.His main research interests include image processing and model processing.
    LIU Li-gang,born in 1975,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include geometry modeling,computational fabrication and shape analysis.
  • Supported by:
    The National Natural Science Foundation of China (61672482).

Abstract: In the scene of image search,due to the randomness of search request,in order to increase the search speed,it is often necessary to preload the entire data set into the running memory.Because the price of running memory with the same capacity is much higher than that of hard disk,reducing the running memory can greatly reduce the cost of image search service.However,if the data is compressed directly,the search accuracy will be greatly reduced.In this case,this paper proposes a content-based ima-ge search framework,which divides data set into groups.Firstly,the neural network is used to extract image features.On the premise of not compressing the features,a heuristic clustering method is used to group the data,ensuring that there is a certain similarity between the data of each data group.For each data group,HNSW algorithm based on graph structure is used to construct index to speed up image query.In this framework,by controlling the number of data blocks accessed during query,the running memory capacity required by the algorithm can be greatly reduced,under the premise of ensuring the accuracy.

Key words: Approximate nearest neighbor matching, Clustering, Image feature extraction, Image Retrieval, Similarity search

CLC Number: 

  • TP391
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