8-5 2-D Non-separable Wavelet Bases for Texture Classification with Genetic Feature Selection

In this paper. the performances of testure classification based on pyramidal and uniform decomposition are comparatively studied with and without feature selection. This comparison using the subband variance as feature explores the dependence among features. It is shown that the main problem when employing 2-D non-separable wavelet transforms for testure classification is the determination of the suitable features and that yields the best classification results. A Mas-Mas algorithm which is a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. Esperimental results have shown the selectivity of the proposed approach and do capture the testure characteristics.