Unsupervised Texture Segmentation by Spectral-Spatial-Independent Clustering
Giuseppe Scarpa, Michal Haindl · 2006
A novel color texture unsupervised segmentation algorithm is presented which processes independently the spectral and spatial information. The algorithm is composed of two parts. The former provides an over-segmentation of the image, such that basic components for each of the textures which are present are extracted. The latter is a region growing algorithm which reduces drastically the number of regions, and provides a region-hierarchical texture clustering. The over-segmentation is achieved by means of a color-based clustering (CBC) followed by a spatial-based clustering (SBC). The SBC, as well as the subsequent growing algorithm, make use of a characterization of the regions based on shape and context. Experimental results are very promising in case of textures which are quite regular