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Learning High-Order MRF Priors of Color Images

2006

Conference Paper

ei


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.

Author(s): McAuley, J. and Caetano, T. and Smola, A. and Franz, MO.
Book Title: ICML 2006
Journal: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)
Pages: 617-624
Year: 2006
Month: June
Day: 0
Editors: Cohen, W. W., A. Moore
Publisher: ACM Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1145/1143844.1143922
Event Name: 23rd International Conference on Machine Learning
Event Place: Pittsburgh, PA, USA

Address: New York, NY, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{3921,
  title = {Learning High-Order MRF Priors of Color Images},
  author = {McAuley, J. and Caetano, T. and Smola, A. and Franz, MO.},
  journal = {Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)},
  booktitle = {ICML 2006},
  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},
  month_numeric = {6}
}