Comments on ``Application of the Conditional Population-Mixture Model to Image Segmentation''

Abstract

In the above correspondence1 a maximum likelihood method is proposed for ``estimating'' class memberships and underlying statistical parameters, within the context of distribution mixtures. In the present comment it is pointed out that biases are incurred in parameter estimation, that the class memberships and parameters are conceptually different, and therefore that the so-called standard mixture likelihood is to be preferred. Also in the correspondence,1 Akaike's information criterion (AIC) is used to choose the number of classes in the mixture. Here a brief theoretical caveat is issued.