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Histogram clustering for unsupervised image segmentation

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

This paper introduces a novel statistical mixture model for probabilistic grouping of distributional (histogram) data. Adopting the Bayesian framework, we propose to perform annealed maximum a posteriori estimation to compute optimal clustering solutions. In order to accelerate the optimization process, an efficient multiscale formulation is developed. We present a prototypical application of this method for the unsupervised segmentation of textured images based on local distributions of Gabor coefficients. Benchmark results indicate superior performance compared to K-means clustering and proximity-based algorithms

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Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.  (Volume:2 )

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