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Unsupervised segmentation of textured images by pairwise data clustering

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3 Author(s)
T. Hofmann ; Inst. fur Inf., Rheinische Friedrich-Wilhelms-Univ., Bonn, Germany ; J. Puzicha ; J. M. Buhmann

A novel approach to unsupervised texture segmentation is presented which is formulated as a combinatorial optimization problem known as pairwise data clustering with a sparse neighborhood structure. Pairwise dissimilarities between texture blocks are measured in terms of distribution differences of multi-resolution features. The feature vectors are based on a Gabor wavelet image representation. To efficiently solve the data clustering problem a deterministic annealing algorithm on the basis of a mean field approximation is derived. An application to collages of Brodatz-like microtexture is demonstrated. The adequacy of the proposed segmentation cost function is statistically validated. The deterministic annealing algorithm outperforms its stochastic variants in terms of quality and efficiency

Published in:

Image Processing, 1996. Proceedings., International Conference on  (Volume:3 )

Date of Conference:

16-19 Sep 1996