Multi-class feature selection for texture classification

Authors:
Xue-wen Chen;Xiangyan Zeng;Deborah van Alphen
Affiliations:
Information and Telecommunication Technology Center, Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, KS 66045, United States;Department of Electrical and Computer Engineering, California State University, Northridge, CA 91330, United States;Department of Electrical and Computer Engineering, California State University, Northridge, CA 91330, United States
Venue:
Pattern Recognition Letters
Year:
2006

Citing 17
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Abstract

In this paper, a multi-class feature selection scheme based on recursive feature elimination (RFE) is proposed for texture classifications. The feature selection scheme is performed in the context of one-against-all least squares support vector machine classifiers (LS-SVM). The margin difference between binary classifiers with and without an associated feature is used to characterize the discriminating power of features for the binary classification. A new criterion of min-max is used to mix the ranked lists of binary classifiers for multi-class feature selection. When compared to the traditional multi-class feature selection methods, the proposed method produces better classification accuracy with fewer features, especially in the case of small training sets.