Abstract
In this paper a rigorous mathematical framework of deterministic annealing and mean-field approximation is presented for a general class of partitioning, clustering and segmentation problems. We describe the canonical way to derive efficient optimization heuristics, which have a broad range of possible applications in computer vision, pattern recognition and data analysis. In addition, we prove novel convergence results. As a major practical application we present a new approach to the problem of unsupervised texture segmentation which relies on statistical tests as a measure of homogeneity. More specifically, this results in a formulation of texture segmentation as a pairwise data clustering problem with a sparse neighborhood structure. We discuss and compare different clustering objective functions, which are systematically derived from invariance principles. The quality of the novel algorithms is empirically evaluated on a large database of Brodatz-like micro-texture mixtures and on a representative set of real-word images.
Supported by the German Research Foundation (DFG # BU 914/3-1) and by the Federal Ministry for Education, Science and Technology (BMBF # 01 M 3021 A/4).
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Hofmann, T., Puzicha, J., Buhmann, J.M. (1997). Deterministic annealing for unsupervised texture segmentation. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_82
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DOI: https://doi.org/10.1007/3-540-62909-2_82
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