A model-fitting approach to cluster validation with application to stochastic model-based image segmentation

A model-fitting approach to the cluster validation problem based upon Akaike´s information criterion (AIC) is proposed. The explicit evaluation of the AIC for the image segmentation problem is achieved through an approximate maximum-likelihood-estimation algorithm. The efficacy of the proposed approach is demonstrated through experimental results for both synthetic mixture data, where the number of clusters is known, and stochastic model-based image segmentation operating on real-world images, for which the number of clusters is unknown. This approach is shown to correctly identify the known number of clusters in the synthetically generated data and to result in good subjective segmentations in aerial photographs