Multi-class feature selection for texture classification

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. (c) 2006 Elsevier B.V. All rights reserved.