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Multitraining support vector machine for image retrieval

Li, J. and Allinson, N. and Tao, D. and Li, Xuelong (2006) Multitraining support vector machine for image retrieval. IEEE Transactions on Image Processing 15 (11), pp. 3597-3601. ISSN 1057-7149.

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Official URL: http://dx.doi.org/10.1109/TIP.2006.881938

Abstract

Relevance feedback (RF) schemes based on support vector machines (SVMs) have been widely used in content-based image retrieval (CBIR). However, the performance of SVM-based RF approaches is often poor when the number of labeled feedback samples is small. This is mainly due to 1) the SVM classifier being unstable for small-size training sets because its optimal hyper plane is too sensitive to the training examples; and 2) the kernel method being ineffective because the feature dimension is much greater than the size of the training samples. In this paper, we develop a new machine learning technique, multitraining SVM (MTSVM), which combines the merits of the cotraining technique and a random sampling method in the feature space. Based on the proposed MTSVM algorithm, the above two problems can be mitigated. Experiments are carried out on a large image set of some 20 000 images, and the preliminary results demonstrate that the developed method consistently improves the performance over conventional SVM-based RFs in terms of precision and standard deviation, which are used to evaluate the effectiveness and robustness of a RF algorithm, respectively.

Item Type: Article
Additional Information: This is an exact copy of a paper published in IEEE Transactions on Image Processing (ISSN 1057-7149). This material is posted here with permission of the IEEE. Copyright © 2006 IEEE.
Keyword(s) / Subject(s): content-based image retrieval (CBIR), multitraining SVM (MTSVM), relevance feedback (RF), support vector machine (SVM)
School or Research Centre: Birkbeck Schools and Research Centres > School of Business, Economics & Informatics > Computer Science and Information Systems
Depositing User: Sandra Plummer
Date Deposited: 30 Jan 2007
Last Modified: 17 Apr 2013 12:33
URI: http://eprints.bbk.ac.uk/id/eprint/453

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