M-band wavelet discrimination of natural textures

The M-band wavelet decomposition, a direct generalization of the standard 2-band wavelet decomposition has been applied to the problem of discriminating natural textures of varying sizes. Regular, M-band filter banks were designed using a genetic algorithm search strategy over the Householder parameter space of M-band wavelets. An exhaustive M-band decomposition was performed on 20 natural textures and energy features were extracted for each decomposed sub-band. The discrimination ability of the extracted features was compared for values of M = 2, 3 and 4. A nearest neighbor algorithm was used to classify a test set of 700 images to an accuracy of 99.5%. The performance was compared with a complete decomposition and decomposition using an irregular M-band filter bank. Statistical tests were used to evaluate the average performance of features extracted from the decomposed sub-bands.