Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

@inproceedings{Krhenbhl2011EfficientII,
  title={Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials},
  author={Philipp Kr{\"a}henb{\"u}hl and Vladlen Koltun},
  booktitle={NIPS},
  year={2011}
}
Most state-of-the-art techniques for multi-class image segmentation and labeling use conditional random fields defined over pixels or image regions. While region-level models often feature dense pairwise connectivity, pixel-level models are considerably larger and have only permitted sparse graph structures. In this paper, we consider fully connected CRF models defined on the complete set of pixels in an image. The resulting graphs have billions of edges, making traditional inference algorithms… CONTINUE READING

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