Robust Image Hashing with Tensor Decomposition
Open accessAbstract
This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to as TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor. Specifically, a stable three-order tensor is first constructed from the normalized image, so as to enhance the robustness of our TD hashing. A popular TD algorithm, called Tucker decomposition, is then exploited to decompose the three-order tensor into a core tensor and three orthogonal factor matrices. As the factor matrices can reflect intrinsic structure of original tensor, hash construction with the factor matrices makes a desirable discrimination of the TD hashing. To examine these claims, there are 14,551 images selected for our experiments. A receiver operating characteristics (ROC) graph is used to conduct theoretical analysis and the ROC comparisons illustrate that the TD hashing outperforms some state-of-the-art algorithms in classification performance between the robustness and discrimination.
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Flagged first, then by citations
Research works printed but not linked: 5
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- Zhenjun Tang (M'15) received the BS and MEng degrees from Guangxi Normal University, Guilin, China, in 2003 and 2006, respectively, and the PhD degree from Shanghai University, Shanghai, China, in 2010. He is now a professor with the Department of Computer Science, Guangxi Nor- mal University. His research interests include image processing and multimedia security. He has contributed more than 50 international jour- nal papers. He is a reviewer of more than 20 SCI journals, such as IEEE/IET journals, Elsevier journals, and Springer journals. He is a member of the IEEE. Lv Chen received the BS degree from Guangxi Normal University, Guilin, China, in 2015. He is working towards the MEng degree in computer science at Guangxi Normal University.
- Shichao Zhang (M'04-SM'04) received the PhD degree in computer science from Deakin Univer- sity, Geelong, VIC, Australia. He is currently a China 1000-Plan Distinguished Professor with the Department of Computer Science, Guangxi Normal University, Guilin, China. He has authored more than 60 international journal papers and 70 international conference papers. His current research interests include data quality and pat- tern discovery. He is a member of the Association for Computing Machinery. As a chief investigator, he has won four Australian Large ARC Grants, three China 863 Pro- grams, two China 973 Programs, and five NSFs of China Grants. He served/serves as an associate editor of the IEEE Transactions on Knowledge and Data Engineering, Knowledge and Information Systems, and the IEEE Intelligent Informatics Bulletin. He served as a PC chair or conference chair for six international conferences. He is a senior member of the IEEE
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“In 2019 [22], they designed a novel image hash by constructing a stable third-order tensor from the normalized image and then decomposing the third-order tensor using a tensor decomposition algorithm known as Tuck decomposition.”
MethodA High-Performance Region Recognition Network-Enhanced Deep CNN for Image Content Perceptual Hashing
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- Authors
- Tang, Z., Chen, L., Zhang, X., & Zhang, S.
- Affiliations
- Guangxi Normal University (CN)
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 2019-03-01
- Volume
- 31
- Issue
- 3
- Pages
- 549-560
- ISSN
- 1041-4347 (ISSN-L)
- 1558-2191
- 2326-3865
- OpenAlex
- W2804853849
- Cited by
- 134
- References
- 65
- Citations by year
- 2026: 7 · 2025: 10 · 2024: 20 · 2023: 20 · 2022: 28 · 2021: 20 · 2020: 18 · 2019: 10 · 2018: 1
- FWCI
- 6.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- IEEE Transactions on Knowledge and Data Engineering (publishedVersion)
- Funders
- National Natural Science Foundation of China (61562007, 61672177, 61762017)
- National Key R & D Program of China (2016YFB1000905)
- Natural Science Foundation of Guangxi Province (2017GXNSFAA198222, 2015GXNSFDA139040)
- Guangxi Science and Technology (GuiKeAD17195062)
- Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing
- Guangxi Key Lab of Multi-source Information Mining & Security (16-A-02-02)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Keywords
- image hashing, tensor decomposition, Tucker decomposition, robustness, discrimination, core tensor, image retrieval
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