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Title:
Accurate and efficient cross-domain visual matching leveraging multiple feature representations
Author: Sun, Gang (1) ; Wang, Shuhui (3) ; Liu, Xuehui (1) ; Huang, Qingming (2) ; Chen, Yanyun (1) ; Wu, Enhua (1)
Issued Date: 2013
Keyword: Visual matching ; Cross-domain ; Multiple features ; Hyperplane hashing
Corresponding Author: Sun, G.(sung@ios.ac.cn)
Publish Place: Springer Verlag
Indexed Type: SCI ; EI
ISSN: 1782789
Department: (1) State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China; (2) University of Chinese Academy of Sciences, Beijing, China; (3) Key Laboratory of Intelligent Information Processing (CAS), Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China; (4) University of Macau, China
Abstract: Cross-domain visual matching aims at finding visually similar images across a wide range of visual domains, and has shown a practical impact on a number of applications. Unfortunately, the state-of-the-art approach, which estimates the relative importance of the single feature dimensions still suffers from low matching accuracy and high time cost. To this end, this paper proposes a novel cross-domain visual matching framework leveraging multiple feature representations. To integrate the discriminative power of multiple features, we develop a data-driven, query specific feature fusion model, which estimates the relative importance of the individual feature dimensions as well as the weight vector among multiple features simultaneously. Moreover, to alleviate the computational burden of an exhaustive subimage search, we design a speedup scheme, which employs hyperplane hashing for rapidly collecting the hard-negatives. Extensive experiments carried out on various matching tasks demonstrate that the proposed approach outperforms the state-of-the-art in both accuracy and efficiency. © 2013 Springer-Verlag Berlin Heidelberg.
English Abstract: Cross-domain visual matching aims at finding visually similar images across a wide range of visual domains, and has shown a practical impact on a number of applications. Unfortunately, the state-of-the-art approach, which estimates the relative importance of the single feature dimensions still suffers from low matching accuracy and high time cost. To this end, this paper proposes a novel cross-domain visual matching framework leveraging multiple feature representations. To integrate the discriminative power of multiple features, we develop a data-driven, query specific feature fusion model, which estimates the relative importance of the individual feature dimensions as well as the weight vector among multiple features simultaneously. Moreover, to alleviate the computational burden of an exhaustive subimage search, we design a speedup scheme, which employs hyperplane hashing for rapidly collecting the hard-negatives. Extensive experiments carried out on various matching tasks demonstrate that the proposed approach outperforms the state-of-the-art in both accuracy and efficiency. © 2013 Springer-Verlag Berlin Heidelberg.
Language: 英语
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Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16556
Appears in Collections:软件所图书馆_会议论文

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Sun, Gang ,Wang, Shuhui ,Liu, Xuehui ,et al. Accurate and efficient cross-domain visual matching leveraging multiple feature representations[C]. 见:.
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