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Discrete Multi-graph Hashing for Large-Scale Visual Search

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Abstract

Hashing has become a promising technique to be applied to the large-scale visual retrieval tasks. Multi-view data has multiple views, providing more comprehensive information. The challenges of using hashing to handle multi-view data lie in two aspects: (1) How to integrate multiple views effectively? (2) How to reduce the distortion error in the quantization stage? In this paper, we propose a novel hashing method, called discrete multi-graph hashing (DMGH), to address the above challenges. DMGH uses a multi-graph learning technique to fuse multiple views, and adaptively learns the weights of each view. In addition, DMGH explicitly minimizes the distortion errors by carefully designing a quantization regularization term. An alternative algorithm is developed to solve the proposed optimization problem. The optimization algorithm is very efficient due to the low-rank property of the anchor graph. The experiments on three large-scale datasets demonstrate the proposed method outperforms the existing multi-view hashing methods.

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Notes

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Acknowledgements

This project is supported by National Natural Science Foundation of China (Nos. 61202439 and 61772561), and partly supported by Scientific Research Fundation of Hunan Provincial Education Department of China (No. 16A008) and Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems (No. 2017TP1016).

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang

  2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang

  3. Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang

  4. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xiaobo Shen

  5. College of Computer Science and Information Technology, Central South University of Forestry and Technology, Changsha, 410004, China

    Jiaohua Qin

  6. School of Traffic & Transportation Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Wei Hao

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  1. Lingyun Xiang
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  2. Xiaobo Shen
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  3. Jiaohua Qin
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  4. Wei Hao
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Correspondence to Xiaobo Shen.

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Xiang, L., Shen, X., Qin, J. et al. Discrete Multi-graph Hashing for Large-Scale Visual Search. Neural Process Lett 49, 1055–1069 (2019). https://doi.org/10.1007/s11063-018-9892-7

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