Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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 |
ISSN | 0162-8828 |
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 |
DOI | 10.1109/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 | |
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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