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Code Consistent Hashing based on Information-theoretic Criterion
Shu Zhang1; Jian Liang1; Ran He1,2; Zhenan Sun1,2
2015-11
发表期刊IEEE Transactions on Big Data (TBD)
卷号1期号:3页码:84-94
摘要
Learning based hashing techniques have attracted broad research interests in the Big Media research area. They aim to
learn compact binary codes which can preserve semantic similarity in the Hamming embedding. However, the discrete constraints
imposed on binary codes typically make hashing optimizations very challenging. In this paper, we present a code consistent hashing
(CCH) algorithm to learn discrete binary hash codes. To form a simple yet efficient hashing objective function, we introduce a new code
consistency constraint to leverage discriminative information and propose to utilize the Hadamard code which favors an informationtheoretic
criterion as the class prototype. By keeping the discrete constraint and introducing an orthogonal constraint, our objective
function can be minimized efficiently. Experimental results on three benchmark datasets demonstrate that the proposed CCH
outperforms state-of-the-art hashing methods in both image retrieval and classification tasks, especially with short binary codes.
关键词Supervised Hashing Binary Codes Code Consistent Constraint Information-theoretic Criterion
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19703
专题智能感知与计算研究中心
通讯作者Ran He
作者单位1.Center for Research on Intelligent Perception and Computing (CRIPAC), National Laboratory of Pattern Recognition(NLPR)
2.Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences (CAS)
推荐引用方式
GB/T 7714
Shu Zhang,Jian Liang,Ran He,et al. Code Consistent Hashing based on Information-theoretic Criterion[J]. IEEE Transactions on Big Data (TBD),2015,1(3):84-94.
APA Shu Zhang,Jian Liang,Ran He,&Zhenan Sun.(2015).Code Consistent Hashing based on Information-theoretic Criterion.IEEE Transactions on Big Data (TBD),1(3),84-94.
MLA Shu Zhang,et al."Code Consistent Hashing based on Information-theoretic Criterion".IEEE Transactions on Big Data (TBD) 1.3(2015):84-94.
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