Nonlinear Asymmetric Multi-Valued Hashing
Da, Cheng1,2; Meng, Gaofeng1; Xiang, Shiming1,2; Ding, Kun1; Xu, Shibiao1; Yang, Qing1; Pan, Chunhong1
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2019-11-01
卷号41期号:11页码:2660-2676
通讯作者Xiang, Shiming(smxiang@nlpr.ia.ac.cn)
摘要Most existing hashing methods resort to binary codes for large scale similarity search, owing to the high efficiency of computation and storage. However, binary codes lack enough capability in similarity preservation, resulting in less desirable performance. To address this issue, we propose Nonlinear Asymmetric Multi-Valued Hashing (NAMVH) supported by two distinct non-binary embeddings. Specifically, a real-valued embedding is used for representing the newly-coming query by an ideally nonlinear transformation. Besides, a multi-integer-embedding is employed for compressing the whole database, which is modeled by Binary Sparse Representation (BSR) with fixed sparsity. With these two non-binary embeddings, NAMVH preserves more precise similarities between data points and enables access to the incremental extension with database samples evolving dynamically. To perform meaningful asymmetric similarity computation for efficient semantic search, these embeddings are jointly learnt by preserving the pairwise label-based similarity. Technically, this results in a mixed integer programming problem, which is efficiently solved by a well-designed alternative optimization method. Extensive experiments on seven large scale datasets demonstrate that our approach not only outperforms the existing binary hashing methods in search accuracy, but also retains their query and storage efficiency.
关键词Asymmetric hashing multi-valued embeddings binary sparse representation nonlinear transformation
DOI10.1109/TPAMI.2018.2867866
关键词[WOS]LEARNING BINARY-CODES ; RANKING ; OBJECT ; SCENE
收录类别SCI
语种英语
资助项目National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[61671451] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61620106003] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[61671451]
项目资助者National Natural Science Foundation of China
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000489838200008
出版者IEEE COMPUTER SOC
七大方向——子方向分类模式识别基础
引用统计
被引频次:6[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/21706
专题模式识别国家重点实验室_先进时空数据分析与学习
空天信息研究中心
模式识别国家重点实验室
通讯作者Xiang, Shiming
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
推荐引用方式
GB/T 7714
Da, Cheng,Meng, Gaofeng,Xiang, Shiming,et al. Nonlinear Asymmetric Multi-Valued Hashing[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2019,41(11):2660-2676.
APA Da, Cheng.,Meng, Gaofeng.,Xiang, Shiming.,Ding, Kun.,Xu, Shibiao.,...&Pan, Chunhong.(2019).Nonlinear Asymmetric Multi-Valued Hashing.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,41(11),2660-2676.
MLA Da, Cheng,et al."Nonlinear Asymmetric Multi-Valued Hashing".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 41.11(2019):2660-2676.
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