Data driven mean-shift belief propagation for non-gaussian MRFs

Minwoo Park, Somesh Kashyap, Robert T Collins, Yanxi Liu
2010 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition  
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
more » ... 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.
doi:10.1109/cvpr.2010.5539946 dblp:conf/cvpr/ParkKCL10 fatcat:4ewnguewdfgl5phhcrinvtrtxe