ML Anthology

Majorization-Minimization Mixture Model Determination in Image Segmentation

Giorgos Sfikas, Christophoros Nikou, Nikolas P. Galatsanos, Christian Heinrich
CVPR 2011 pp. 2169-2176
doi:10.1109/CVPR.2011.5995349 /cvpr/2011/sfikas2011cvpr-majorization/

Abstract

A new Bayesian model for image segmentation based on a Gaussian mixture model is proposed. The model structure allows the automatic determination of the number of segments while ensuring spatial smoothness of the final output. This is achieved by defining two separate mixture weight sets: the first set of weights is spatially variant and incorporates an MRF edge-preserving smoothing prior; the second set of weights is governed by a Dirichlet prior in order to prune unnecessary mixture components. The model is trained using variational inference and the Majorization-Minimization (MM) algorithm, resulting in closed-form parameter updates. The algorithm was successfully evaluated in terms of various segmentation indices using the Berkeley image data base.

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Cite

Text

Sfikas et al. "Majorization-Minimization Mixture Model Determination in Image Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011. doi:10.1109/CVPR.2011.5995349

Markdown

[Sfikas et al. "Majorization-Minimization Mixture Model Determination in Image Segmentation." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2011.](https://mlanthology.org/cvpr/2011/sfikas2011cvpr-majorization/) doi:10.1109/CVPR.2011.5995349

BibTeX

@inproceedings{sfikas2011cvpr-majorization,
  title     = {{Majorization-Minimization Mixture Model Determination in Image Segmentation}},
  author    = {Sfikas, Giorgos and Nikou, Christophoros and Galatsanos, Nikolas P. and Heinrich, Christian},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2011},
  pages     = {2169-2176},
  doi       = {10.1109/CVPR.2011.5995349},
  url       = {https://mlanthology.org/cvpr/2011/sfikas2011cvpr-majorization/}
}