We derive a criterion for the selection of random field models for color images. Models are defined in terms of sets of neighbors that characterize interactions within and between bands of a color image. A Bayesian approach is used to select from a set of models the model which maximizes the posterior probability of the model given the image data. For efficiency, maximum likelihood parameter estimates are computed in the frequency domain. The selection of appropriate random field models is particularly important for color images because of the large number of possible within-band and between-band interactions. We demonstrate the usefulness of the method for designing image models for unsupervised color image segmentation.

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