CASIA OpenIR  > 模式识别国家重点实验室  > 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, Weihua1; Hu, Weiming2; Peng, Houwen1; Ding, Xinmiao1; Maybank, Steve3
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2017-12-01
Volume39Issue:12Pages:2554-2560
SubtypeArticle
AbstractIn 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.
KeywordMulti-instance Learning Multi-view Sparse Representation Dictionary Learning
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2017.2669303
WOS KeywordIMAGE RETRIEVAL ; RECOGNITION ; CLASSIFICATION ; ALGORITHM
Indexed BySCI
Language英语
Funding OrganizationNatural Science Foundation of China(61370038 ; 973 basic research program of China(2014CB349303) ; CAS(XDB02070003) ; Youth Innovation Promotion Association, CAS ; U1636218 ; 61472421 ; 61571045)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000414395400017
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/19566
Collection模式识别国家重点实验室_视频内容安全
Affiliation1.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
Recommended Citation
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.
Files in This Item: Download All
File Name/Size DocType Version Access License
Libing-Multi-view mu(352KB)期刊论文作者接受稿开放获取CC BY-NC-SAView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Li, Bing]'s Articles
[Yuan, Chunfeng]'s Articles
[Xiong, Weihua]'s Articles
Baidu academic
Similar articles in Baidu academic
[Li, Bing]'s Articles
[Yuan, Chunfeng]'s Articles
[Xiong, Weihua]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Li, Bing]'s Articles
[Yuan, Chunfeng]'s Articles
[Xiong, Weihua]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Libing-Multi-view multi-instance learning based on joint sparse representation and multi-view dictionary learning.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.