Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

Understand

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.