中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 视频内容安全团队
Horror Image Recognition Based on Context-Aware Multi-Instance Learning

文献类型：期刊论文

作者Li, Bing1; Xiong, Weihua1; Wu, Ou1; Hu, Weiming1; Maybank, Stephen2; Yan, Shuicheng3; Bing Li
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2015-12-01
卷号24期号:12页码:5193-5205
关键词Horror image recognition context-aware multi-instance learning visual saliency
英文摘要Horror content sharing on the Web is a growing phenomenon that can interfere with our daily life and affect the mental health of those involved. As an important form of expression, horror images have their own characteristics that can evoke extreme emotions. In this paper, we present a novel context-aware multi-instance learning (CMIL) algorithm for horror image recognition. The CMIL algorithm identifies horror images and picks out the regions that cause the sensation of horror in these horror images. It obtains contextual cues among adjacent regions in an image using a random walk on a contextual graph. Borrowing the strength of the fuzzy support vector machine (FSVM), we define a heuristic optimization procedure based on the FSVM to search for the optimal classifier for the CMIL. To improve the initialization of the CMIL, we propose a novel visual saliency model based on the tensor analysis. The average saliency value of each segmented region is set as its initial fuzzy membership in the CMIL. The advantage of the tensor-based visual saliency model is that it not only adaptively selects features, but also dynamically determines fusion weights for saliency value combination from different feature subspaces. The effectiveness of the proposed CMIL model is demonstrated by its use in horror image recognition on two large-scale image sets collected from the Internet.
WOS标题词Science & Technology ; Technology
类目[WOS]Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
研究领域[WOS]Computer Science ; Engineering
关键词[WOS]SALIENT REGION DETECTION ; VISUAL-ATTENTION ; FEAR-ACQUISITION ; COLOR ; TEXTURE ; SEGMENTATION ; RETRIEVAL ; EMOTION ; MODEL ; INFORMATION
收录类别SCI ; SSCI
语种英语
WOS记录号WOS:000362488900007
公开日期2015-12-24
源URL[http://ir.ia.ac.cn/handle/173211/10029]  
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
通讯作者Bing Li
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ London, Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HK, England
3.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117583, Singapore
推荐引用方式
GB/T 7714		 Li, Bing,Xiong, Weihua,Wu, Ou,et al. Horror Image Recognition Based on Context-Aware Multi-Instance Learning[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2015,24(12):5193-5205.
APA Li, Bing.,Xiong, Weihua.,Wu, Ou.,Hu, Weiming.,Maybank, Stephen.,...&Bing Li.(2015).Horror Image Recognition Based on Context-Aware Multi-Instance Learning.IEEE TRANSACTIONS ON IMAGE PROCESSING,24(12),5193-5205.
MLA Li, Bing,et al."Horror Image Recognition Based on Context-Aware Multi-Instance Learning".IEEE TRANSACTIONS ON IMAGE PROCESSING 24.12(2015):5193-5205.

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。