Most state-of-the-art techniques for multi-class image segmentation and labeling
use conditional random fields defined over pixels or image regions. While regionlevel
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.
