  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 impractical. Our main
contribution is a highly efficient approximate inference algorithm for fully
connected CRF models in which the pairwise edge potentials are defined by a
linear combination of Gaussian kernels. Our experiments demonstrate that dense
connectivity at the pixel level substantially improves segmentation and
labeling accuracy.
