Unsupervised texture segmentation in a deterministic annealingframework |
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Authors: | Hofmann T. Puzicha J. Buhmann J.M. |
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Affiliation: | Dept. of Brain & Cognitive Sci., MIT, Cambridge, MA; |
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Abstract: | We present a novel optimization framework for unsupervised texture segmentation that relies on statistical tests as a measure of homogeneity. Texture segmentation is formulated as a data clustering problem based on sparse proximity data. Dissimilarities of pairs of textured regions are computed from a multiscale Gabor filter image representation. We discuss and compare a class of clustering objective functions which is systematically derived from invariance principles. As a general optimization framework, we propose deterministic annealing based on a mean-field approximation. The canonical way to derive clustering algorithms within this framework as well as an efficient implementation of mean-field annealing and the closely related Gibbs sampler are presented. We apply both annealing variants to Brodatz-like microtexture mixtures and real-word images |
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