Histogram Clustering for Unsupervised Image Segmentation

  title={Histogram Clustering for Unsupervised Image Segmentation},
  author={Jan Puzicha and Joachim M. Buhmann and Thomas Hofmann},
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… CONTINUE READING
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