Cluster validation for unsupervised stochastic model-based image segmentation

Image segmentation is an important processing stage in many image analysis problems. Often this must be done in an unsupervised fashion in that training data is not available. A major obstacle in such applications is the determination of the number of distinct regions present in an image. This problem, called the cluster validation problem, remains essentially unsolved. We investigate the cluster validation problem associated with the use of a previously developed unsupervised segmentation algorithm based upon the expectation-maximization (EM) algorithm. We consider several well-known information-theoretic criteria (ICs) as candidate solutions. We show that these criteria generally provide inappropriate results. As an alternative we propose a model-fitting technique in which the complete data log-likelihood functional is modeled as an exponential function in the number of classes acting, and the class estimate is related to the rise time. This new validation technique is shown to be robust and outperform the ICs in our experiments