Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning

doi:10.1109/TPAMI.2017.2669303

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 视频内容安全

	Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning
	Li, Bing1 ; Yuan, Chunfeng1 ; Xiong, Weihua 1; Hu, Weiming2 ; Peng, Houwen1 ; Ding, Xinmiao 1; Maybank, Steve 3
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2017-12-01
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
DOI	10.1109/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	被引频次：23[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/19566
专题	多模态人工智能系统全国重点实验室_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
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	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.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Libing-Multi-view mu（352KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载