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Application of the Conditional Population-Mixture Model to Image Segmentation
April 1983 (vol. 5 no. 4)
pp. 428-433
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.
Citation:
Stanley L. Sclove, "Application of the Conditional Population-Mixture Model to Image Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, no. 4, pp. 428-433, April 1983, doi:10.1109/TPAMI.1983.4767412
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