Local spectra features extraction based on 2D pseudo-Wigner distribution for texture analysis

This paper addresses the generic issue of texture image analysis using local spectra features that are based on joint space/spatial-frequency (S/SF) analysis methods. The two-dimensional (2D) Wigner distribution (WD) and its discrete implementation pseudo-Wigner distribution (PWD) are discussed. A set of new local spectral features are calculated from the peaks of the 2D PWD. To assess the feasibility of these features for discriminating different textures by a stable set of feature values, they are applied to texture segmentation using a standard fuzzy c-means clustering algorithm. The obtained results show that PWD allows us to extract the intrinsic features of the texture image, and that using the proposed local spectra features yields satisfactory texture segmentation results