Unsupervised Texture Segmentation in a Deterministic Annealing Framework (1998)
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BibTeX
@MISC{Hofmann98unsupervisedtexture,
author = {Thomas Hofmann and Jan Puzicha and Joachim M. Buhmann},
title = {Unsupervised Texture Segmentation in a Deterministic Annealing Framework},
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 multi-scale 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 micro-texture mixtures and real-word images.







