Issue No. 04 - April (1983 vol. 5)
Stanley L. Sclove , Department of Quantitative Methods, University of Illinois at Chicago, Chicago, IL 60680.
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.
S. L. Sclove, "Application of the Conditional Population-Mixture Model to Image Segmentation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 5, no. , pp. 428-433, 1983.