ML Anthology

Histogram Clustering for Unsupervised Image Segmentation

Jan Puzicha, Joachim M. Buhmann, Thomas Hofmann
CVPR 1999 pp. 2602-2608
doi:10.1109/CVPR.1999.784981 /cvpr/1999/puzicha1999cvpr-histogram/

Abstract

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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Cite

Text

Puzicha et al. "Histogram Clustering for Unsupervised Image Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.784981

Markdown

[Puzicha et al. "Histogram Clustering for Unsupervised Image Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/puzicha1999cvpr-histogram/) doi:10.1109/CVPR.1999.784981

BibTeX

@inproceedings{puzicha1999cvpr-histogram,
  title     = {{Histogram Clustering for Unsupervised Image Segmentation}},
  author    = {Puzicha, Jan and Buhmann, Joachim M. and Hofmann, Thomas},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {2602-2608},
  doi       = {10.1109/CVPR.1999.784981},
  url       = {https://mlanthology.org/cvpr/1999/puzicha1999cvpr-histogram/}
}