Texture analysis and classification with the nonseparable wavelet transform

This paper investigates the application of nonseparable wavelet transform for the texture characterization. On a set of 21 Brodatz textures we have performed traditional dyadic wavelet decomposition (in two levels) and nonseparable quincunx decomposition (in six levels), testing their ability to classify textures in: (a) normal working conditions, (b) noisy environment, and (c) with respect to rotation. Our experiments have shown that the quincunx transform is appropriate for characterization of noisy data, small number of resolution levels and shorter feature vectors and rotationally invariant description.