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Locality sensitive hashing with bit selection

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Abstract

Locality sensitive hashing (LSH), one of the most popular hashing techniques, has attracted considerable attention for nearest neighbor search in the field of image retrieval. It can achieve promising performance only if the number of the generated hash bits is large enough. However, more hash bits assembled to the binary codes contain massive redundant information and require more time cost and storage spaces. To alleviate this limitation, we propose a novel bit selection framework to pick important bits out of the hash bits generated by hashing techniques. Within the bit selection framework, we further exploit eleven evaluation criteria to measure the importance and similarity of each bit generated by LSH, so that the bits with high importance and less similarity are selected to assemble new binary codes. To demonstrate the effectiveness of the proposed framework of bit selection, we evaluated the proposed framework with the evaluation criteria on five commonly used data sets. Experimental results show the proposed bit selection framework works effectively in different cases, and the performance of LSH has not been degraded significantly after redundant hash bits reduced by the evaluation criteria.

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Acknowledgments

The authors would like to thank the anonymous referees and the editors for their valuable comments and suggestions, helping to improve the paper significantly. This work was partially supported by the national NSF of China (NSFC) (61976195) and the NSF of Zhejiang Province (LY18F020019).

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Authors and Affiliations

  1. Department of Computer Science, Zhejiang Normal University, Jinhua, 321004, People’s Republic of China

    Wenhua Zhou & Xin Chen

  2. Department of Computer Science, Shaoxing University, Shaoxing, 312000, People’s Republic of China

    Huawen Liu

  3. School of Information Engineering, Huzhou University, Huzhou, 313000, People’s Republic of China

    Jungang Lou

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  1. Wenhua Zhou
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  2. Huawen Liu
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  3. Jungang Lou
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  4. Xin Chen
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Correspondence to Huawen Liu or Xin Chen.

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This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

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Cite this article

Zhou, W., Liu, H., Lou, J. et al. Locality sensitive hashing with bit selection. Appl Intell 52, 14724–14738 (2022). https://doi.org/10.1007/s10489-022-03546-9

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