Revisiting uncertainty in graph cut solutions

  title={Revisiting uncertainty in graph cut solutions},
  author={Daniel Tarlow and Ryan P. Adams},
  journal={2012 IEEE Conference on Computer Vision and Pattern Recognition},
Graph cuts is a popular algorithm for finding the MAP assignment of many large-scale graphical models that are common in computer vision. While graph cuts is powerful, it does not provide information about the marginal probabilities associated with the solution it finds. To assess uncertainty, we are forced to fall back on less efficient and inexact inference algorithms such as loopy belief propagation, or use less principled surrogate representations of uncertainty such as the min-marginal… CONTINUE READING
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