The problem of image segmentation is considered in the context of a mixture of probability distributions. The segments fall into classes. A probability distribution is associated with each class of segment. Parametric families of distributions are considered, a set of parameter values being associated with each class. With each observation is associated an unobservable label, indicating from which class the observation arose. Segmentation algorithms are obtained by applying a method of iterated maximum likelihood to the resulting likelihood function. A numerical example is given. Choice of the number of classes, using Akaike's information criterion (AIC) for model identification, is illustrated.

When within-object interpixel correlation varies appreciably from object to object, it may be important for the classifier to utilize this correlation, as well as the mean and variance of pixel intensities. In this correspondence interpixel correlation is brought into the classification scheme by means of a two-dimensional Markov model.