Comparative study of different spatial/spatial-frequency methods (Gabor filters, wavelets, wavelets packets) for texture segmentation/classification

The interest manifested into texture recognition has had a substantial growth since these past few years. Much research is focused on a joint space/spatial-frequency representation of images. This paper describes the comparison between different spatial/spatial-frequency methods involving Gabor filters and wavelets. Two applications are considered: image segmentation and texture classification/recognition. It has been seen that results can depend significantly on the method used to decompose the signal. Two aspects are mainly taken into account: the effect of an over-sampling of the frequency domain by a bank of filters, and the ability of 1D wavelets to segment/classify oriented textures. An improvement using angular filters with separable wavelets is demonstrated, while no noticeable amelioration is observed using Battle-Lemarie wavelets, as compared to Gabor filters.