Multi-Perspective Cost-Sensitive Context-Aware Multi-Instance Sparse Coding and Its Application to Sensitive Video Recognition
文献类型：期刊论文
| 作者 | Hu, Weiming1 ; Ding, Xinmiao2; Li, Bing1; Wang, Jianchao1; Gao, Yan1; Wang, Fangshi3; Maybank, Stephen4
|
| 刊名 | IEEE TRANSACTIONS ON MULTIMEDIA
 |
| 出版日期 | 2016 |
| 卷号 | 18期号:1页码:76-89 |
| 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 |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications |
| 研究领域[WOS] | Computer Science ; Telecommunications |
| 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 |
| WOS记录号 | WOS:000367139700008 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/10651]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_视频内容安全团队 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
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。