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1999
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Histogram Clustering for Unsupervised Image Segmentation

9 years 11 months ago
Histogram Clustering for Unsupervised Image Segmentation
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 e cient 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 coe cients. Benchmark results indicate superior performance compared to K means clustering and proximity-based algorithms.
Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 1999
Where CVPR
Authors Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann
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