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A novel image hashing with low-rank sparse matrix decomposition and feature distance

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Abstract

Image hashing is an efficient technique of image processing for various applications, such as retrieval, copy detection and authentication. In this paper, we design a novel image hashing algorithm using LRSMD (low-rank sparse matrix decomposition). Firstly, an input image is preprocessed by interpolation, Gaussian blur and color space conversion. Next, the preprocessed image is fed into the LRSMD for learning a low-rank matrix. Then, statistical features of non-overlapping blocks in the low-rank matrix are extracted. Finally, the hash code is obtained by calculating feature distances. Various experiments are done on public datasets to demonstrate the robustness and discrimination of the proposed algorithm. The results show that the proposed algorithm outperforms several advanced algorithms in balancing the performances of robustness and discrimination.

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Data availability

The image databases used to support the findings of this study can be downloaded from the public websites whose hyperlinks are provided in the references.

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Acknowledgements

This work is partially supported by the Guangxi Natural Science Foundation (2024GXNSFBA010191), the National Natural Science Foundation of China (62272111, 62302108), Guangxi “Bagui Scholar” Team for Innovation and Research, Guangxi Talent Highland Project of Big Data Intelligence and Application, Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing, and the Innovation Project of Guangxi Graduate Education (YCBZ2023083, XYCBZ2024022). Zhenjun Tang is the corresponding author. Many thanks to the referees for their helpful suggestions.

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Authors and Affiliations

  1. Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004, China

    Zixuan Yu, Zhenjun Tang, Xiaoping Liang, Hanyun Zhang, Ronghai Sun & Xianquan Zhang

  2. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Zixuan Yu, Zhenjun Tang, Xiaoping Liang, Hanyun Zhang, Ronghai Sun & Xianquan Zhang

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  1. Zixuan Yu
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Correspondence to Zhenjun Tang.

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Yu, Z., Tang, Z., Liang, X. et al. A novel image hashing with low-rank sparse matrix decomposition and feature distance. Vis Comput 41, 1987–1998 (2025). https://doi.org/10.1007/s00371-024-03517-w

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