A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
暂无分享,去创建一个
Sebastian Nowozin | Nikos Komodakis | Christoph Schnörr | Carsten Rother | Bogdan Savchynskyy | Thorben Kröger | Jörg H. Kappes | Fred A. Hamprecht | Dhruv Batra | Björn Andres | Jan Lellmann | Sungwoong Kim | Bernhard X. Kausler | C. Rother | N. Komodakis | S. Nowozin | Dhruv Batra | C. Schnörr | Björn Andres | F. Hamprecht | Bogdan Savchynskyy | Sungwoong Kim | J. Lellmann | Thorben Kröger
[1] Eric P. Xing,et al. An Augmented Lagrangian Approach to Constrained MAP Inference , 2011, ICML.
[2] Ullrich Köthe,et al. Globally Optimal Closed-Surface Segmentation for Connectomics , 2012, ECCV.
[3] Pushmeet Kohli,et al. Robust Higher Order Potentials for Enforcing Label Consistency , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[4] Anton Osokin,et al. Fast Approximate Energy Minimization with Label Costs , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[5] Tomás Werner,et al. A Linear Programming Approach to Max-Sum Problem: A Review , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[6] Christoph Schnörr,et al. A Study of Parts-Based Object Class Detection Using Complete Graphs , 2010, International Journal of Computer Vision.
[7] Nikos Komodakis,et al. MRF Energy Minimization and Beyond via Dual Decomposition , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[8] N. Metropolis,et al. An Efficient Heuristic Procedure for Partitioning Graphs , 2017 .
[9] J. Besag. On the Statistical Analysis of Dirty Pictures , 1986 .
[10] Monique Guignard-Spielberg,et al. Lagrangean decomposition: A model yielding stronger lagrangean bounds , 1987, Math. Program..
[11] George Papandreou,et al. Perturb-and-MAP random fields: Using discrete optimization to learn and sample from energy models , 2011, 2011 International Conference on Computer Vision.
[12] Ulrik Brandes,et al. On Modularity Clustering , 2008, IEEE Transactions on Knowledge and Data Engineering.
[13] Bogdan Savchynskyy,et al. Getting Feasible Variable Estimates from Infeasible Ones: MRF Local Polytope Study , 2012, 2013 IEEE International Conference on Computer Vision Workshops.
[14] Sebastian Nowozin,et al. A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[15] Vladimir Kolmogorov,et al. Convergent Tree-Reweighted Message Passing for Energy Minimization , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Ullrich Köthe,et al. A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness , 2012, ECCV.
[17] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[18] Joseph Naor,et al. A Linear Programming Formulation and Approximation Algorithms for the Metric Labeling Problem , 2005, SIAM J. Discret. Math..
[19] Thomas Stützle,et al. Efficient Stochastic Local Search for MPE Solving , 2005, IJCAI.
[20] Judea Pearl,et al. Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.
[21] Ullrich Köthe,et al. Probabilistic image segmentation with closedness constraints , 2011, 2011 International Conference on Computer Vision.
[22] Ullrich Köthe,et al. An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM , 2010, DAGM-Symposium.
[23] 大西 仁,et al. Pearl, J. (1988, second printing 1991). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan-Kaufmann. , 1994 .
[24] Gerhard Reinelt,et al. Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[25] Pushmeet Kohli,et al. Making the right moves: Guiding alpha-expansion using local primal-dual gaps , 2011, CVPR 2011.
[26] Thorsten Koch,et al. Branching rules revisited , 2005, Oper. Res. Lett..
[27] Alan C. Evans,et al. BrainWeb: Online Interface to a 3D MRI Simulated Brain Database , 1997 .
[28] Vladimir Kolmogorov,et al. Dynamic Tree Block Coordinate Ascent , 2011, ICML.
[29] 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..
[30] Gerhard Reinelt,et al. Globally Optimal Image Partitioning by Multicuts , 2011, EMMCVPR.
[31] Daniel Cremers,et al. A Survey and Comparison of Discrete and Continuous Multi-label Optimization Approaches for the Potts Model , 2013, International Journal of Computer Vision.
[32] Ullrich Köthe,et al. The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models , 2012, ECCV.
[33] Pushmeet Kohli,et al. Reduce, reuse & recycle: Efficiently solving multi-label MRFs , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[34] Christoph Schnörr,et al. MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves , 2014, ECCV Workshops.
[35] Andrew Blake,et al. Digital tapestry [automatic image synthesis] , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[36] William T. Freeman,et al. Constructing free-energy approximations and generalized belief propagation algorithms , 2005, IEEE Transactions on Information Theory.
[37] Vladimir Kolmogorov,et al. Computing geodesics and minimal surfaces via graph cuts , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.
[38] Brian W. Kernighan,et al. An efficient heuristic procedure for partitioning graphs , 1970, Bell Syst. Tech. J..
[39] Michael I. Jordan. Graphical Models , 1998 .
[40] Andrew C. Gallagher,et al. Inference for order reduction in Markov random fields , 2011, CVPR 2011.
[41] David Sontag,et al. Efficiently Searching for Frustrated Cycles in MAP Inference , 2012, UAI.
[42] Sebastian Nowozin,et al. Decision tree fields , 2011, 2011 International Conference on Computer Vision.
[43] Christoph Schnörr,et al. Continuous Multiclass Labeling Approaches and Algorithms , 2011, SIAM J. Imaging Sci..
[44] Yuval Rabani,et al. An improved approximation algorithm for multiway cut , 1998, STOC '98.
[45] Gerhard Reinelt,et al. Higher-order segmentation via multicuts , 2013, Comput. Vis. Image Underst..
[46] Martin J. Wainwright,et al. MAP estimation via agreement on trees: message-passing and linear programming , 2005, IEEE Transactions on Information Theory.
[47] Lena Gorelick,et al. Submodularization for Binary Pairwise Energies , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[48] Ullrich Köthe,et al. 3D segmentation of SBFSEM images of neuropil by a graphical model over supervoxel boundaries , 2012, Medical Image Anal..
[49] Jörg H. Kappes,et al. OpenGM: A C++ Library for Discrete Graphical Models , 2012, ArXiv.
[50] Nikos Komodakis,et al. Approximate Labeling via Graph Cuts Based on Linear Programming , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[51] Anand D. Sarwate,et al. On Measure Concentration of Random Maximum A-Posteriori Perturbations , 2013, ICML.
[52] Sebastian Nowozin,et al. Higher-Order Correlation Clustering for Image Segmentation , 2011, NIPS.
[53] Michael Jünger,et al. Lifting and separation procedures for the cut polytope , 2014, Math. Program..
[54] Richard Szeliski,et al. A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[55] Daniel Prusa,et al. Universality of the Local Marginal Polytope , 2013, CVPR.
[56] Lars Otten,et al. Anytime AND/OR depth-first search for combinatorial optimization , 2011, AI Commun..
[57] Tomás Werner,et al. Universality of the Local Marginal Polytope , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[58] Vladimir Kolmogorov,et al. Optimizing Binary MRFs via Extended Roof Duality , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[59] Sebastian Nowozin,et al. Variable grouping for energy minimization , 2011, CVPR 2011.
[60] Dhruv Batra,et al. MaxFlow Revisited: An Empirical Comparison of Maxflow Algorithms for Dense Vision Problems , 2012, BMVC.
[61] Endre Boros,et al. A graph cut algorithm for higher-order Markov Random Fields , 2011, 2011 International Conference on Computer Vision.
[62] Yair Weiss,et al. Minimizing and Learning Energy Functions for Side-Chain Prediction , 2007, RECOMB.
[63] Vladimir Kolmogorov,et al. Submodular decomposition framework for inference in associative Markov networks with global constraints , 2011, CVPR 2011.
[64] Christoph Schnörr,et al. A bundle approach to efficient MAP-inference by Lagrangian relaxation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[65] Vladimir Kolmogorov,et al. Comparison of Energy Minimization Algorithms for Highly Connected Graphs , 2006, ECCV.
[66] Éva Tardos,et al. Approximation algorithms for classification problems with pairwise relationships: metric labeling and Markov random fields , 2002, JACM.
[67] Nikos Komodakis,et al. Beyond Loose LP-Relaxations: Optimizing MRFs by Repairing Cycles , 2008, ECCV.
[68] Alexei A. Efros,et al. Recovering Occlusion Boundaries from an Image , 2011, International Journal of Computer Vision.
[69] Ivan Kovtun,et al. Partial Optimal Labeling Search for a NP-Hard Subclass of (max, +) Problems , 2003, DAGM-Symposium.
[70] Stephen Gould,et al. Decomposing a scene into geometric and semantically consistent regions , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[71] Vladimir Kolmogorov,et al. What energy functions can be minimized via graph cuts? , 2002, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[72] Tommi S. Jaakkola,et al. Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations , 2007, NIPS.
[73] Andrew Blake,et al. Fusion Moves for Markov Random Field Optimization , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[74] Sebastian Nowozin,et al. Structured Learning and Prediction in Computer Vision , 2011, Found. Trends Comput. Graph. Vis..
[75] Christoph Schnörr,et al. Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation , 2013, NIPS.
[76] Olga Veksler,et al. Fast approximate energy minimization via graph cuts , 2001, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[77] Andrew W. Fitzgibbon,et al. Global stereo reconstruction under second order smoothness priors , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[78] Pushmeet Kohli,et al. Dynamic Hybrid Algorithms for MAP Inference in Discrete MRFs , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[79] Christoph Schnörr,et al. Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing , 2012, UAI.
[80] Nir Friedman,et al. Towards an Integrated Protein-Protein Interaction Network: A Relational Markov Network Approach , 2006, J. Comput. Biol..