GENETIC FEATURE-SELECTION FOR TEXTURE CLASSIFICATION USING 2-D NONSEPARABLE WAVELET BASES

In this paper, the performances of texture classification based on pyr amidal and uniform decomposition are comparatively studied with and wi thout 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 t exture classification is the determination of the suitable Features th at yields the best classification results. A Max-Max algorithm which i s a novel evaluation function based on genetic algorithms is presented to evaluate the classification performance of each subset of selected features. It is shown that the performance with feature selection in which only about half of features are selected is comparable to that w ithout feature selection. Moreover, the discriminatory characteristics of texture spread more in low-pass bands and the features extracted f rom the pyramidal decomposition are more representative than those fro m the uniform decomposition. Experimental results have verified the se lectivity of the proposed approach and its texture capturing character istics.