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Horror Image Recognition Based on Context-Aware Multi-Instance Learning
Li, Bing1; Xiong, Weihua1; Wu, Ou1; Hu, Weiming1; Maybank, Stephen2; Yan, Shuicheng3; Bing Li
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
2015-12-01
Volume24Issue:12Pages:5193-5205
SubtypeArticle
AbstractHorror 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.
KeywordHorror Image Recognition Context-aware Multi-instance Learning Visual Saliency
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIP.2015.2479400
WOS KeywordSALIENT REGION DETECTION ; VISUAL-ATTENTION ; FEAR-ACQUISITION ; COLOR ; TEXTURE ; SEGMENTATION ; RETRIEVAL ; EMOTION ; MODEL ; INFORMATION
Indexed BySCI ; SSCi
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000362488900007
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10029
Collection模式识别国家重点实验室_视频内容安全
Corresponding AuthorBing Li
Affiliation1.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
Recommended Citation
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.
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