A Sparse Support Vector Machine Approach to
Region-Based Image Categorization
Siemens Medical Solutions, Inc.
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.
Full Paper in Color
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.
Copyright 2005 IEEE.
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March 22, 2005