Image segmentation using clustering with saddle point detection

We discuss a novel statistical framework for image segmentation based on nonparametric clustering. By employing the mean shift procedure for analysis, image regions are identified as clusters in the joint color-spatial domain. To measure the significance of each cluster we use a test statistics that compares the estimated density of the cluster mode with the estimated density on the cluster boundary. The cluster boundary in the color domain is defined by saddle points lying on the cluster borders defined in the spatial domain. The proposed technique compares favorably to other segmentation methods described in literature.