Unsupervised Texture Segmentation in a Deterministic Annealing Framework

Authors:
Thomas Hofmann;Jan Puzicha;Joachim M. Buchmann
Affiliations:
Massachussetts Institute of Technology;Rheinische Friedrich-Wilhelms-Universität, Germany;Rheinische Friedrich-Wilhelms-Universität, Germany
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1998

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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.