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

This paper addresses the generic issue of textured image analysis using local spectra features that are based on space/spatial frequency analysis methods. The 2D Wigner-distribution and its discrete implementation pseudo-Wigner-distribution (PWD) are discussed. A set of new local spectral features are derived from a simple decorrelation procedure (principal component analysis) of the PWD. In order to assess the feasibility of the features for characterizing local texture properties, texture segmentation experiments were carried out using these features with the help of the fuzzy-c mean clustering algorithm. The segmentation results show that PWD allows one to extract the intrinsic features of texture image regions, and that using the proposed local spectral features yields satisfactory texture segmentation results