A nonparametric approach to linear feature extraction; application to classification of binary synthetic textures

A nonparametric approach to linear feature extraction is presented. The theoretical background is introduced with a derivation of the equation that gives the best scalar extractor according to Patrick-Fischer distance. The outlines of the implementation are given. The method is applied to the classification of binary synthetic textures with natural visual aspect. The performances of the proposed method are shown to be better than the Fisher discriminant-analysis-based classifier. Concluding remarks are given for future improvements, further applications, and theoretical discussion.<>