A cluster grouping technique for texture segmentation

We propose an algorithm for texture segmentation based on a divide-and-conquer strategy of statistical modeling. Selected sets of Gaussian clusters, estimated via Expectation Maximization on the texture features, are grouped together to form composite texture classes. Our cluster grouping technique exploits the inherent local spatial correlation among posterior distributions of clusters belonging to the same texture class. Despite its simplicity, this algorithm can model even very complex distributions, typical of natural outdoor images.