Data Driven Mean-Shift Belief Propagation For non-Gaussian MRFs

We introduce a novel data-driven mean-shift belief propagation (DDMSBP) method for non-Gaussian MRFs, which often arise in computer vision applications. With the aid of scale space theory, optimization of non-Gaussian, multimodal MRF models using DDMSBP becomes less sensitive to local maxima. This is a significant improvement over standard BP inference, and extends the range of methods that are computationally tractable. In particular, when pair-wise potentials are Gaussians, the time complexity of DDMSBP becomes bilinear in the numbers of states and nodes in the MRF. Experimental results from simulation and non-rigid deformable neuroimage registration demonstrate that our method is faster and more accurate than state-ofthe- art inference algorithms.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Minwoo Park, S. Kashyap, R. Collins, and Y. Liu
• Keywords: Belief Propagation, CVPR 2010, Markov Random Fields (MRF)