@inproceedings{3921,
  title = {Learning High-Order MRF Priors of Color Images},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  abstract = {In this paper, we use large neighborhood Markov random fields to learn rich prior
  models of color images.  Our approach extends the monochromatic Fields of Experts
  model (Roth and Blackwell, 2005) to color images. In the Fields of Experts model, the curse
  of dimensionality due to very large clique sizes is circumvented by parameterizing the
  potential functions according to a product of experts. We introduce several
  simplifications of the original approach by Roth and Black which allow us to cope with
  the increased clique size (typically 3x3x3 or 5x5x3 pixels) of color images.
  Experimental results are presented for image denoising which evidence improvements over
  state-of-the-art monochromatic image priors.},
  pages = {617-624},
  editors = {Cohen, W. W., A. Moore},
  publisher = {ACM Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {New York, NY, USA},
  month = jun,
  year = {2006},
  slug = {3921},
  author = {McAuley, J. and Caetano, T. and Smola, A. and Franz, MO.},
  month_numeric = {6}
}