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

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A Sparse Support Vector Machine Approach to Region-Based Image Categorization
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 demonstra...
Jinbo Bi, Yixin Chen, James Ze Wang
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2005
Where CVPR
Authors Jinbo Bi, Yixin Chen, James Ze Wang
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