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A General Framework for Deep Supervised Discrete Hashing

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Abstract

With the rapid growth of image and video data on the web, hashing has been extensively studied for image or video search in recent years. Benefiting from recent advances in deep learning, deep hashing methods have shown superior performance over the traditional hashing methods. However, there are some limitations of previous deep hashing methods (e.g., the semantic information is not fully exploited). In this paper, we develop a general deep supervised discrete hashing framework based on the assumption that the learned binary codes should be ideal for classification. Both the similarity information and the classification information are used to learn the hash codes within one stream framework. We constrain the outputs of the last layer to be binary codes directly, which is rarely investigated in deep hashing algorithms. Besides, both the pairwise similarity information and the triplet ranking information are exploited in this paper. In addition, two different loss functions are presented: \({l_2}\) loss and hinge loss, which are carefully designed for the classification term under the one stream framework. Because of the discrete nature of hash codes, an alternating minimization method is used to optimize the objective function. Experimental results have shown that our approach outperforms current state-of-the-art methods on benchmark datasets.

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Acknowledgements

This work was partially supported by the Natural Science Foundation of China (Grant Nos. U1836217, 61702513, 61721004, and 61427811). This work was also partially supported by CAS-AIR and Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project ) (No. 2019JZZY010119).

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Authors and Affiliations

  1. Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, CASIA, Beijing, China

    Qi Li, Zhenan Sun, Ran He & Tieniu Tan

  2. Artificial Intelligence Research, CAS, Qingdao, China

    Qi Li

  3. Center for Excellence in Brain Science and Intelligence Technology, CAS, Beijing, China

    Zhenan Sun, Ran He & Tieniu Tan

  4. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Qi Li, Zhenan Sun, Ran He & Tieniu Tan

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  1. Qi Li
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Correspondence to Zhenan Sun.

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Communicated by Li Liu, Matti Pietikäinen, Jie Qin, Jie Chen, Wanli Ouyang, Luc Van Gool.

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Li, Q., Sun, Z., He, R. et al. A General Framework for Deep Supervised Discrete Hashing. Int J Comput Vis 128, 2204–2222 (2020). https://doi.org/10.1007/s11263-020-01327-w

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