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Unsupervised Texture Segmentation in a Deterministic Annealing Framework
August 1998 (vol. 20 no. 8)
pp. 803-818

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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Index Terms:
Image segmentation, pairwise clustering, deterministic annealing, EM algorithm, Gabor filters.
Citation:
Thomas Hofmann, Jan Puzicha, Joachim M. Buhmann, "Unsupervised Texture Segmentation in a Deterministic Annealing Framework," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 8, pp. 803-818, Aug. 1998, doi:10.1109/34.709593
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