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Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition
Hu, Weiming1; Ding, Xinmiao2; Li, Bing1; Wang, Jianchao1; Gao, Yan1; Wang, Fangshi3; Maybank, Stephen4
AbstractWith the development of video-sharing websites, P2P, micro-blog, mobile WAP websites, and so on, sensitive videos can be more easily accessed. Effective sensitive video recognition is necessary for web content security. Among web sensitive videos, this paper focuses on violent and horror videos. Based on color emotion and color harmony theories, we extract visual emotional features from videos. A video is viewed as a bag and each shot in the video is represented by a key frame which is treated as an instance in the bag. Then, we combine multi-instance learning (MIL) with sparse coding to recognize violent and horror videos. The resulting MIL-based model can be updated online to adapt to changing web environments. We propose a cost-sensitive context-aware multi-instance sparse coding (MI-SC) method, in which the contextual structure of the key frames is modeled using a graph, and fusion between audio and visual features is carried out by extending the classic sparse coding into cost-sensitive sparse coding. We then propose a multi-perspective multi-instance joint sparse coding (MI-J-SC) method that handles each bag of instances from an independent perspective, a contextual perspective, and a holistic perspective. The experiments demonstrate that the features with an emotional meaning are effective for violent and horror video recognition, and our cost-sensitive context-aware MI-SC and multi-perspective MI-J-SC methods outperform the traditional MIL methods and the traditional SVM and KNN-based methods.
KeywordCost-sensitive Context-aware Multi-instance Sparse Coding (Mi-sc) Horror Video Recognition Multi-perspective Multi-instance Joint Sparse Coding (Mi-j-sc) Video Emotional Feature Extraction Violent Video Recognition
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Funding Organization973 Basic Research Program of China(2014CB349303) ; Natural Science Foundation of China(61472421 ; CAS Center for Excellence in Brain Science and Intelligence Technology ; Guangdong Natural Science Foundation(S2012020011081) ; 61303086)
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000367139700008
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Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
2.Shandong Inst Business & Technol, Yantai 264005, Peoples R China
3.Beijing Jiaotong Uni, Sch Software Engn, Beijing 100044, Peoples R China
4.Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Hu, Weiming,Ding, Xinmiao,Li, Bing,et al. Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2016,18(1):76-89.
APA Hu, Weiming.,Ding, Xinmiao.,Li, Bing.,Wang, Jianchao.,Gao, Yan.,...&Maybank, Stephen.(2016).Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition.IEEE TRANSACTIONS ON MULTIMEDIA,18(1),76-89.
MLA Hu, Weiming,et al."Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition".IEEE TRANSACTIONS ON MULTIMEDIA 18.1(2016):76-89.
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