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Unsupervised texture segmentation in a deterministic annealing framework

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3 Author(s)
T. Hofmann ; Dept. of Brain & Cognitive Sci., MIT, Cambridge, MA, USA ; J. Puzicha ; J. M. Buhmann

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

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:20 ,  Issue: 8 )