Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition

doi:10.1109/TMM.2015.2496372

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-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition
	Hu, Weiming1 ; Ding, Xinmiao 2; Li, Bing1 ; Wang, Jianchao 1; Gao, Yan 1; Wang, Fangshi 3; Maybank, Stephen 4
发表期刊	IEEE TRANSACTIONS ON MULTIMEDIA
	2016
卷号	18 期号:1 页码:76-89
文章类型	Article
摘要	With 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.
关键词	Cost-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标题词	Science & Technology ; Technology
DOI	10.1109/TMM.2015.2496372
关键词[WOS]	VIOLENCE DETECTION ; COLOR PREFERENCE ; REPRESENTATION ; CLASSIFICATION ; CATEGORIZATION ; AUDIO ; INFORMATION ; CHILDHOOD ; FEATURES ; EMOTION
收录类别	SCI
语种	英语
项目资助者	973 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研究方向	Computer Science ; Telecommunications
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS记录号	WOS:000367139700008
引用统计	被引频次：16[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/173211/10651
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.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
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	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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