Deep Supervised Discrete Hashing
By Qi Li, Zhenan Sun, Ran He, Tien-Ping Tan. Neural Information Processing Systems, 2017
334 citations recorded by the source corpus.
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. Benefit from recent advances in deep learning, deep hashing methods have achieved promising results for image retrieval. 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 deep supervised discrete hashing algorithm based on the assumption that the learned binary codes should be ideal for classification. Both the pairwise label 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 algorithm. 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 method outperforms current state-of-the-art methods on benchmark datasets.
Subfields: Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/71ea34ce0283ffa46b899dcea5565a2614da5bec
How researchers see this paper
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In Computer Vision and Pattern Recognition
- Lawrence Carin
- Shiv Ram Dubey
- Dinghan Shen
- Ricardo Henao
- Guoyin Wang
- Chunyuan Li
- Shifeng Zhang
- Qinliang Su
Citations in the corpus
- NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (2018, background): “Models with discrete random variables have attracted much attention in the deep learning community (Jang et al., 2016; Maddison et al., 2016; van den Oord et al., 2017; Li et al., 2017; Shu and Nakayama, 2017).”
- A Decade Survey of Content Based Image Retrieval Using Deep Learning (2020, methodology): “The deep supervised hashing [89] and deep supervised discrete hashing [88], [95] approaches generate the supervised binary hash code for image retrieval.”
- Pairwise Teacher-Student Network for Semi-Supervised Hashing (2019, methodology): “Results on supervised hashing methods like DSH [15], DPSH [13], DSDH [12], DISH [29], DMDH [5] and MIHash [2] are also proposed for comparison.”