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2010
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Data Driven Mean-Shift Belief Propagation For non-Gaussian MRFs

10 years 6 months ago
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.
Minwoo Park, S. Kashyap, R. Collins, and Y. Liu
Added 05 Apr 2010
Updated 24 May 2010
Type Conference
Year 2010
Where CVPR
Authors Minwoo Park, S. Kashyap, R. Collins, and Y. Liu
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