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Domain transfer SVM for video concept detection

Duan, L. and Tsang, I.W. and Xu, D. and Maybank, Stephen J. (2009) Domain transfer SVM for video concept detection. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009: CVPR 2009, 20-25 June 2009, Miami, U.S..

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Cross-domain learning methods have shown promising results by leveraging labeled patterns from auxiliary domains to learn a robust classifier for target domain, which has a limited number of labeled samples. To cope with the tremendous change of feature distribution between different domains in video concept detection, we propose a new cross-domain kernel learning method. Our method, referred to as Domain Transfer SVM (DTSVM), simultaneously learns a kernel function and a robust SVM classifier by minimizing both the structural risk functional of SVM and the distribution mismatch of labeled and unlabeled samples between the auxiliary and target domains. Comprehensive experiments on the challenging TRECVID corpus demonstrate that DTSVM outperforms existing cross-domain learning and multiple kernel learning methods.

Item Type: Conference or Workshop Item (Paper)
Keyword(s) / Subject(s): Broadcasting, Humans, Kernel, Learning systems, Multimedia communication, Robustness, Support vector machine classification, Support vector machines, Testing, Training data
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Administrator
Date Deposited: 05 Nov 2012 11:15
Last Modified: 17 Apr 2013 12:26

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