CASIA OpenIR  > 智能感知与计算研究中心
Deep Supervised Discrete Hashing
Qi Li; Zhenan Sun; Ran He; Tieniu Tan
2017-12
Conference NameNeural Information Processing Systems (NIPS)
Conference Date2017-12
Conference PlaceLong Beach, America
AbstractWith 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 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.
Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19693
Collection智能感知与计算研究中心
AffiliationCenter for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Qi Li,Zhenan Sun,Ran He,et al. Deep Supervised Discrete Hashing[C],2017.
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