Texture Image Segmentation using Reduced Gabor Filter Set and Mean Shift Clustering

This paper presents an unsupervised texture image segmentation algorithm using reduced Gabor filter set and mean shift clustering. Two criteria are proposed in order to construct a feature space of reduced dimensions for texture image segmentation, based on selected Gabor filter subset from a predefined Gabor filter set. An unsupervised clustering algorithm using the mean shift clustering method is then applied to the reduced feature space to obtain the number of clusters, i.e. the number of texture regions. A simple Euclidean distance classification scheme is used to group the pixels into corresponding texture regions. Experiments on a mixture of Brodatz textures or mosaic of textures generated by random field model show the proposed algorithm of using the reduced Gabor filter set and mean shift clustering gives satisfactory results in terms of the number of regions and region shapes.