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    Horror image recognition based on context-aware multi-instance learning

    Li, B. and Xiong, W. and Wu, O. and Hu, W. and Maybank, Stephen J. and Yan, S. (2015) Horror image recognition based on context-aware multi-instance learning. IEEE Transactions on Image Processing 24 (12), pp. 5193-5205. ISSN 1057-7149.

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    Abstract

    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 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.

    Metadata

    Item Type: Article
    Additional Information: (c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Keyword(s) / Subject(s): horror image recognition, context-aware multi-instance learning, visual saliency
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Professor Stephen Maybank
    Date Deposited: 06 Oct 2015 09:49
    Last Modified: 06 Oct 2015 09:49
    URI: http://eprints.bbk.ac.uk/id/eprint/12950

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