CASIA OpenIR  > 模式识别国家重点实验室  > 先进时空数据分析与学习
Nonlinear Asymmetric Multi-Valued Hashing
Da, Cheng1,2; Meng, Gaofeng1; Xiang, Shiming1,2; Ding, Kun1; Xu, Shibiao1; Yang, Qing1; Pan, Chunhong1
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN0162-8828
2019-11-01
Volume41Issue:11Pages:2660-2676
Corresponding AuthorXiang, Shiming(smxiang@nlpr.ia.ac.cn)
AbstractMost 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.
KeywordAsymmetric hashing multi-valued embeddings binary sparse representation nonlinear transformation
DOI10.1109/TPAMI.2018.2867866
WOS KeywordLEARNING BINARY-CODES ; RANKING ; OBJECT ; SCENE
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000489838200008
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21706
Collection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
Corresponding AuthorXiang, Shiming
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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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