English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Learning High-Order MRF Priors of Color Images

McAuley, J., Caetano T, Smola, A., & Franz, M. (2006). Learning High-Order MRF Priors of Color Images. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006), 617-624.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
McAuley, J, Author
Caetano T, Smola, A, Author
Franz, MO1, Author           
Cohen A. Moore, W. W., Editor
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

Content

show
hide
Free keywords: -
 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.

Details

show
hide
Language(s):
 Dates: 2006-06
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: URI: http://www.icml2006.org/icml2006/home.html
DOI: 10.1145/1143844.1143922
BibTex Citekey: 3921
 Degree: -

Event

show
hide
Title: 23rd International Conference on Machine Learning
Place of Event: Pittsburgh, PA, USA
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006)
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 617 - 624 Identifier: -