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-of-the-art inference algorithms.

[1]  Yanxi Liu,et al.  Efficient mean shift belief propagation for vision tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Nassir Navab,et al.  Dense image registration through MRFs and efficient linear programming , 2008, Medical Image Anal..

[3]  Yanxi Liu,et al.  DICOVERY OF "BIOMARKERS" FOR ALZHEIMER'S DISEASE PREDICTION FROM STRUCTURAL MR IMAGES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  A.S. Willsky,et al.  Nonparametric belief propagation for self-calibration in sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[5]  Daniel P. Huttenlocher,et al.  Efficient Belief Propagation for Early Vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[6]  Arno Klein,et al.  Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration , 2009, NeuroImage.

[7]  Guido Gerig,et al.  User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability , 2006, NeuroImage.

[8]  Nikos Komodakis,et al.  Approximate Labeling via Graph Cuts Based on Linear Programming , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Yanxi Liu,et al.  FEATURE-BASED VS. INTENSITY-BASED BRAIN IMAGE REGISTRATION: COMPREHENSIVE COMPARISON USING MUTUAL INFORMATION , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[10]  Tony X. Han,et al.  Efficient Nonparametric Belief Propagation with Application to Articulated Body Tracking , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[11]  Yanxi Liu,et al.  Discriminative MR Image Feature Analysis for Automatic Schizophrenia and Alzheimer's Disease Classification , 2004, MICCAI.

[12]  Martial Hebert,et al.  Smoothing-based Optimization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  James M. Coughlan,et al.  Shape Matching with Belief Propagation: Using Dynamic Quantization to Accomodate Occlusion and Clutter , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[14]  David A. Forsyth,et al.  Finding and tracking people from the bottom up , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[15]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[16]  Meritxell Bach Cuadra,et al.  Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images , 2005, IEEE Transactions on Medical Imaging.

[17]  Anton van den Hengel,et al.  Fast global kernel density mode seeking with application to localization and tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[18]  Michael Isard,et al.  PAMPAS: real-valued graphical models for computer vision , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[19]  Peter Salamon,et al.  Facts, Conjectures, and Improvements for Simulated Annealing , 1987 .

[20]  Michael Isard,et al.  Nonparametric belief propagation , 2010, Commun. ACM.

[21]  William T. Freeman,et al.  Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology , 1999, Neural Computation.

[22]  Tom Minka,et al.  Expectation Propagation for approximate Bayesian inference , 2001, UAI.

[23]  Gregory A. Miller,et al.  Classification of functional brain images with a spatio-temporal dissimilarity map , 2006, NeuroImage.

[24]  Yanxi Liu,et al.  A Lattice-Based MRF Model for Dynamic Near-Regular Texture Tracking , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Michael Isard,et al.  Attractive People: Assembling Loose-Limbed Models using Non-parametric Belief Propagation , 2003, NIPS.

[26]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[27]  Pedro F. Felzenszwalb,et al.  Efficient belief propagation for early vision , 2004, CVPR 2004.

[28]  Václav Hlavác,et al.  Efficient MRF Deformation Model for Non-Rigid Image Matching , 2007, CVPR.

[29]  Yanxi Liu,et al.  Deformed Lattice Detection in Real-World Images Using Mean-Shift Belief Propagation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.