A Sparse Support Vector Machine Approach to
Region-Based Image Categorization

Jinbo Bi
Siemens Medical Solutions, Inc.

Yixin Chen
University of New Orleans

James Z. Wang
The Pennsylvania State University

Automatic image categorization using low-level features is a challenging research topic in computer vision. In this paper, we formulate the image categorization problem as a multiple-instance learning (MIL) problem by viewing an image as a bag of instances, each corresponding to a region obtained from image segmentation. We propose a new solution to the resulting MIL problem. Unlike many existing MIL approaches that rely on the diverse density framework, our approach performs an effective feature mapping through a chosen metric distance function. Thus the MIL problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method.

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Citation: Jinbo Bi, Yixin Chen and James Z. Wang, ``A Sparse Support Vector Machine Approach to Region-Based Image Categorization,'' Proc. International Conference on Computer Vision and Pattern Recognition, vol. I, pp. 1121-1128, San Diego, CA, IEEE, June 2005.

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Last Modified: March 22, 2005