Optimal learning high-order Markov random fields priors of colour image
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...[Show more]
|Collections||ANU Research Publications|
|Source:||Computer Vision - Proceedings of the 8th Asian Conference on Computer Vision (ACCV 2007)|
|01_Zhang_Optimal_learning_high-order_2007.pdf||607 kB||Adobe PDF||Request a copy|
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