Feature subspaces selection via one-class SVM: Application to textured image segmentation

This paper presents a feature subspaces selection method which uses an ensemble of one-class SVMs. The objective is to improve or preserve the performance of a decision system in the presence of noise, loss of information or feature non-stationarity. The proposed method consists in first generating an ensemble of feature subspaces from the initial full-dimensional space, and then making the decision by using only the subspaces which are supposed to be immune to the non-stationary disturbance. One particularity of this method is that we use the one-class SVM ensemble to carry out the feature selection and the classification tasks at the same time. Textured image segmentation constitutes an appropriate application for the evaluation of the proposed approach. The experimental results demonstrate the effectiveness of the decision system that we have developed.