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Asymmetric similarity-preserving discrete hashing for image retrieval

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Abstract

Hashing methods have been widely studied in the image research community due to their low storage and fast computation. However, generating compact hash codes is still a challenging task. In this paper, we propose a novel Asymmetric Similarity-Preserving Discrete Hashing (ASPDH) method to learn compact binary codes for image retrieval. Specifically, the pairwise similarity matrix is approximated in the asymmetric learning manner with two different real-valued embeddings. In addition, ASPDH constructs two distinct hash functions from the kernel feature and label consistency embeddings. Therefore, similarity preservation and hash code learning can be simultaneously achieved and interactively optimized, which further improves the discriminative capability of the learned binary codes. Then, a well-designed iterative algorithm is developed to efficiently solve the optimization problem, resulting in high-quality binary codes with reduced quantization errors. Extensive experiments on three public datasets show the rationality and effectiveness of our proposed method.

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Data Availability

The data of MIRFlickr that support the findings of this study are openly available at: https://press.liacs.nl/mirflickr/, reference number [44]. The data of MNIST that support the findings of this study are available at: http://yann.lecun.com/exdb/mnist/, reference number [45]. The data of NUS-WIDE that support the findings of this study are available at: https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html, reference number [46].

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Acknowledgements

This work is supported by the Natural Science Foundation of Shandong Province (No. ZR2020LZH008, ZR2021MF118, ZR2019MF071), the Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (NO.2021CXGC010506, NO.2021SFGC0104), the National Natural Science Foundation of China (No.91846205).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250300, China

    Xiuxiu Ren & Xiangwei Zheng

  2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan, 250300, China

    Xiuxiu Ren & Xiangwei Zheng

  3. School of Software, Shandong University, Jinan, 250101, China

    Lizhen Cui

  4. Institute of Data Science and Statistics, Shanghai University of Finance and Economics, Shanghai, 200433, China

    Gang Wang

  5. School of Computing and Mathematical Sciences, University of Leicester, Leicester, United Kingdom

    Huiyu Zhou

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  1. Xiuxiu Ren
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Correspondence to Xiangwei Zheng.

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Ren, X., Zheng, X., Cui, L. et al. Asymmetric similarity-preserving discrete hashing for image retrieval. Appl Intell 53, 12114–12131 (2023). https://doi.org/10.1007/s10489-022-04167-y

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