Efficient Belief Propagation with Learned Higher-Order Markov Random Fields

@inproceedings{Lan2006EfficientBP,
  title={Efficient Belief Propagation with Learned Higher-Order Markov Random Fields},
  author={Xiangyang Lan and Stefan Roth and Daniel P. Huttenlocher and Michael J. Black},
  booktitle={ECCV},
  year={2006}
}
Belief propagation (BP) has become widely used for low-level vision problems and various inference techniques have been proposed for loopy graphs. These methods typically rely on ad hoc spatial priors such as the Potts model. In this paper we investigate the use of learned models of image structure, and demonstrate the improvements obtained over previous ad hoc models for the image denoising problem. In particular, we show how both pairwise and higher-order Markov random fields with learned… CONTINUE READING
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