Efficient Belief Propagation for Vision Using Linear Constraint Nodes

Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, and has been successfully applied to several important computer vision problems. However, pairwise interactions are often insufficient to capture the full statistics of the problem. Higher-order interactions are sometimes required. Unfortunately, the complexity of belief propagation is exponential in the size of the largest clique. In this paper, we introduce a new technique to compute belief propagation messages in time linear with respect to clique size for a large class of potential functions over real-valued variables. We demonstrate this technique in two applications. First, we perform efficient inference in graphical models where the spatial prior of natural images is captured by 2 ? 2 cliques. This approach shows significant improvement over the commonly used pairwise-connected models, and may benefit a variety of applications using belief propagation to infer images or range imag...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Brian Potetz
• Keywords: Belief Propagation, Belief Propagation Messages, Computer Vision, CVPR 2007, Demonstrate Significant Improvement, Largest Clique, Pairwise Connected Markov