Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)
Philipp Krähenbühl, Vladlen Koltun
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 con- siderably 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 experi- ments demonstrate that dense connectivity at the pixel level substantially improves segmentation and labeling accuracy.