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Nonlinear Asymmetric Multi-Valued Hashing

文献类型：期刊论文

作者Da, Cheng1,2; Meng, Gaofeng1; Xiang, Shiming1,2; Ding, Kun1; Xu, Shibiao1; Yang, Qing1; Pan, Chunhong1
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2019-11-01
卷号41期号:11页码:2660-2676
关键词Asymmetric hashing multi-valued embeddings binary sparse representation nonlinear transformation
ISSN号0162-8828
DOI10.1109/TPAMI.2018.2867866
通讯作者Xiang, Shiming(smxiang@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词LEARNING BINARY-CODES ; RANKING ; OBJECT ; SCENE
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]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000489838200008
出版者IEEE COMPUTER SOC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/21706]  
专题自动化研究所_模式识别国家重点实验室_遥感图像处理团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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