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Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning
Li, Bing1; Yuan, Chunfeng1; Xiong, Weihua1; Hu, Weiming2; Peng, Houwen1; Ding, Xinmiao1; Maybank, Steve3
2017-12-01
发表期刊IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷号39期号:12页码:2554-2560
文章类型Article
摘要In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm ((MIL)-I-2) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse epsilon-graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the M2IL. Experiments and analyses in many practical applications prove the effectiveness of the M2IL.
关键词Multi-instance Learning Multi-view Sparse Representation Dictionary Learning
WOS标题词Science & Technology ; Technology
DOI10.1109/TPAMI.2017.2669303
关键词[WOS]IMAGE RETRIEVAL ; RECOGNITION ; CLASSIFICATION ; ALGORITHM
收录类别SCI
语种英语
项目资助者Natural Science Foundation of China(61370038 ; 973 basic research program of China(2014CB349303) ; CAS(XDB02070003) ; Youth Innovation Promotion Association, CAS ; U1636218 ; 61472421 ; 61571045)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000414395400017
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/19566
专题模式识别国家重点实验室_视频内容安全
作者单位1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat,Natl Lab Pattern Recognit, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100049, Peoples R China
3.Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
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Li, Bing,Yuan, Chunfeng,Xiong, Weihua,et al. Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2017,39(12):2554-2560.
APA Li, Bing.,Yuan, Chunfeng.,Xiong, Weihua.,Hu, Weiming.,Peng, Houwen.,...&Maybank, Steve.(2017).Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,39(12),2554-2560.
MLA Li, Bing,et al."Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 39.12(2017):2554-2560.
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