Self-organized Clustering of Mixture Models for Combined Color And Texture Segmentation
The segmentation of images based on color and texture cues is formulated as a clustering problem in the joint color and texture space. Small image patches are grouped together on the basis of local color and spatial frequency statistics which is captured by Gaussian mixture models in feature space. The locality of segments in feature space is taken into account by the topological organization of the cluster structure which corresponds to self-organizing maps. The clusters define a one dimensional chain in the space of mixture models, which favors an ordering of the groups with similar color and texture distributions. The probabilistic model is tested by segmentation experiments on images from the Corel data base.