Optimal learning high-order Markov random fields priors of colour image

Authors:
Ke Zhang;Huidong Jin;Zhouyu Fu;Nianjun Liu
Affiliations:
Research School of Information Sciences and Engineering, Australian National University and National ICT Australia, Canberra Lab, ACT, Australia;Research School of Information Sciences and Engineering, Australian National University and National ICT Australia, Canberra Lab, ACT, Australia;Research School of Information Sciences and Engineering, Australian National University and National ICT Australia, Canberra Lab, ACT, Australia;Research School of Information Sciences and Engineering, Australian National University and National ICT Australia, Canberra Lab, ACT, Australia
Venue:
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Year:
2007

Citing 6
Cited 0

Line search algorithms with guaranteed sufficient decrease

ACM Transactions on Mathematical Software (TOMS)
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling

International Journal of Computer Vision
Learning Low-Level Vision

International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Fields of Experts: A Framework for Learning Image Priors

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning high-order MRF priors of color images

ICML '06 Proceedings of the 23rd international conference on Machine learning
Image denoising using scale mixtures of Gaussians in the wavelet domain

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we present an optimised learning algorithm for learning the parametric prior models for high-order Markov random fields (MRF) of colour images. Compared to the priors used by conventional low-order MRFs, the learned priors have richer expressive power and can capture the statistics of natural scenes. Our proposed optimal learning algorithm is achieved by simplifying the estimation of partition function without compromising the accuracy of the learned model. The parameters in MRF colour image priors are learned alternatively and iteratively in an EM-like fashion by maximising their likelihood. We demonstrate the capability of the proposed learning algorithm of high-order MRF colour image priors with the application of colour image denoising. Experimental results show the superior performance of our algorithm compared to the state-of-the-art of colour image priors in [1], although we use a much smaller training image set.