Efficient piecewise learning for conditional random fields

@article{Alahari2010EfficientPL,
  title={Efficient piecewise learning for conditional random fields},
  author={Karteek Alahari and Christopher Russell and Philip H. S. Torr},
  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  year={2010},
  pages={895-901}
}
Conditional Random Field models have proved effective for several low-level computer vision problems. Inference in these models involves solving a combinatorial optimization problem, with methods such as graph cuts, belief propagation. Although several methods have been proposed to learn the model parameters from training data, they suffer from various drawbacks. Learning these parameters involves computing the partition function, which is intractable. To overcome this, state-of-the-art… CONTINUE READING

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