CASIA OpenIR  > 模式识别国家重点实验室  > 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, Xinmiao2; Li, Bing1; Wang, Jianchao1; Gao, Yan1; Wang, Fangshi3; Maybank, Stephen4
2016
发表期刊IEEE TRANSACTIONS ON MULTIMEDIA
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
DOI10.1109/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
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/10651
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.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
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.
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
HU_TMM.pdf(2469KB)期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Hu, Weiming]的文章
[Ding, Xinmiao]的文章
[Li, Bing]的文章
百度学术
百度学术中相似的文章
[Hu, Weiming]的文章
[Ding, Xinmiao]的文章
[Li, Bing]的文章
必应学术
必应学术中相似的文章
[Hu, Weiming]的文章
[Ding, Xinmiao]的文章
[Li, Bing]的文章
相关权益政策
暂无数据
收藏/分享
文件名: HU_TMM.pdf
格式: Adobe PDF
此文件暂不支持浏览
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。