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Issue No.11 - November (2002 vol.24)
pp: 1517-1520
ABSTRACT
<p><b>Abstract</b>—We propose a method for choosing the number of colors or true gray levels in an image; this allows fully automatic segmentation of images. Our underlying probability model is a hidden Markov random field. Each number of colors considered is viewed as corresponding to a statistical model for the image, and the resulting models are compared via approximate Bayes factors. The Bayes factors are approximated using BIC (Bayesian Information Criterion), where the required maximized likelihood is approximated by the Qian-Titterington pseudolikelihood. We call the resulting criterion PLIC (Pseudolikelihood Information Criterion). We also discuss a simpler approximation, MMIC (Marginal Mixture Information Criterion), which is based only on the marginal distribution of pixel values. This turns out to be useful for initialization and it also has moderately good performance by itself when the amount of spatial dependence in an image is low. We apply PLIC and MMIC to a medical image segmentation problem.</p>
INDEX TERMS
BIC, color image quantization, ICM algorithm, image segmentation, Markov random field, medical image, mixture model, posterior model probability, pseudolikelihood, satellite image.
CITATION
Derek C. Stanford, Adrian E. Raftery, "Approximate Bayes Factors for Image Segmentation: The Pseudolikelihood Information Criterion (PLIC)", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.24, no. 11, pp. 1517-1520, November 2002, doi:10.1109/TPAMI.2002.1046170
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