Utilizing Variational Optimization to Learn Markov Random Fields

@article{Tappen2007UtilizingVO,
  title={Utilizing Variational Optimization to Learn Markov Random Fields},
  author={Marshall F. Tappen},
  journal={2007 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2007},
  pages={1-8}
}
Markov random field, or MRF, models are a powerful tool for modeling images. While much progress has been made in algorithms for inference in MRFs, learning the parameters of an MRF is still a challenging problem. In this paper, we show how variational optimization can be used to learn the parameters of an MRF. This method for learning, which we refer to as variational mode learning, finds the MRF parameters by minimizing a loss function that penalizes the difference between ground-truth images… CONTINUE READING
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