Learning optimized MAP estimates in continuously-valued MRF models

  title={Learning optimized MAP estimates in continuously-valued MRF models},
  author={Kegan G. G. Samuel and Marshall F. Tappen},
  journal={2009 IEEE Conference on Computer Vision and Pattern Recognition},
We present a new approach for the discriminative training of continuous-valued Markov Random Field (MRF) model parameters. In our approach we train the MRF model by optimizing the parameters so that the minimum energy solution of the model is as similar as possible to the ground-truth. This leads to parameters which are directly optimized to increase the quality of the MAP estimates during inference. Our proposed technique allows us to develop a framework that is flexible and intuitively easy… CONTINUE READING
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