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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 timedoi:10.1109/cvpr.2010.5539946 dblp:conf/cvpr/ParkKCL10 fatcat:4ewnguewdfgl5phhcrinvtrtxe