Abstract
The design of optimal feature sets for visual classification problems is still one of the most challenging topics in the area of computer vision. In this work, we propose a new algorithm that computes optimal features, in the minimum Bayes error sense, for visual recognition tasks. The algorithm now proposed combines the fast convergence rate of feature selection (FS) procedures with the ability of feature extraction (FE) methods to uncover optimal features that are not part of the original basis function set. This leads to solutions that are better than those achievable by either FE or FS alone, in a small number of iterations, making the algorithm scalable in the number of classes of the recognition problem. This property is currently only available for feature extraction methods that are either sub-optimal or optimal under restrictive assumptions that do not hold for generic imagery. Experimental results show significant improvements over these methods, either through much greater robustness to local minima or by achieving significantly faster convergence. (C) 2006 Elsevier B.V. All rights reserved.