Mixture models and the segmentation of multimodal textures

A problem with using mixture-of-Gaussian models for unsupervised texture segmentation is that a “multimodal” texture (such as can often be encountered in natural images) cannot be well represented by a single Gaussian cluster. We propose a divide-and-conquer method that groups together Gaussian clusters (estimated via Expectation Maximization) into homogeneous texture classes. This method allows to successfully segment even rather complex textures, as demonstrated by experimental tests on natural images