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 (LSSVM). 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 multiclass 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. 2006 Elsevier B.V. All rights reserved.