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
Abstract:
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
(PDF, 0.14MB)
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.
Copyright 2005 IEEE.
Personal use of this
material is permitted. However, permission to reprint/republish this
material for advertising or promotional purposes or for creating new
collective works for resale or redistribution to servers or lists, or
to reuse any copyrighted component of this work in other works, must
be obtained from the IEEE.
Last Modified:
March 22, 2005
© 2005