A MODEL-FITTING APPROACH TO CLUSTER VALIDATION WITH APPLICATION TO STOCHASTIC MODEL-BASED IMAGE SEGMENTATION

An unsupervised stochastic model-based image segmentation technique requires the model parameters for the various image classes in an observed image to be estimated directly from the image. In this work, a clustering scheme is used for the model parameter estimation. Most of the existing clustering procedures require prior knowledge of the number of classes which is often, as in unsupervised image segmentation, unavailable and has to be estimated. This problem is known as the cluster validation problem. For unsupervised image segmentation, the solution of this problem directly affects the quality of the segmentation. In this work, we propose a model-fitting approach to the cluster validation problem based upon Akaike's information criterion (AIC), and we demonstrate its efficacy and robustness through experimental results for synthetic mixture data and image data. © 1990 IEEE