Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 31, Issue 3, Pages 549-560Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2018.2837745
Keywords
Image hashing; tensor construction; tensor decomposition; Tucker decomposition
Categories
Funding
- National Natural Science Foundation of China [61562007, 61672177, 61762017]
- National Key R&D Program of China [2016YFB1000905]
- Guangxi Bagui Scholar Teams for Innovation and Research
- Guangxi Natural Science Foundation [2017GXNSFAA198222, 2015GXNSFDA139040]
- Project of Guangxi Science and Technology [Gui-KeAD17195062]
- 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]
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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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