Deep Supervised Discrete Hashing

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	Deep Supervised Discrete Hashing
	Qi Li; Zhenan Sun; Ran He; Tieniu Tan
	2017-12
会议日期	2017-12
会议地点	Long Beach, America
英文摘要	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 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.
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/19693]
专题	自动化研究所_智能感知与计算研究中心
作者单位	Center for Research on Intelligent Perception and Computing, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
推荐引用方式 GB/T 7714	Qi Li,Zhenan Sun,Ran He,et al. Deep Supervised Discrete Hashing[C]. 见:. Long Beach, America. 2017-12.