Texture-based segmentation using markov random field models and approximate Bayesian estimators based on trees
We describe segmentation based on textures using the label and image model of D. Gemanet al., “Boundary Detection by Constrained Optimization,”IEEE Trans. Pattern Analysis and Machine Intelligence, 12(7):609–628, July 1990. We replace their maximuma posteriori estimation criterion with a Bayesian estimator that minimizes the sum of the pixel misclassification probabilities. The new estimation goal allows the use of a different computational algorithm, which is deterministic rather than random, based on approximating lattices by trees. An example demonstrating an accurate segmentation of a collage of Brodatz textures is included.
KeywordsImage textures Image segmentation Markov random fields Kolmogorov-Smirnov statistic
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