13.3.12.6 MRF Optimization, Energy Minimization

Komodakis, N.[Nikos], Tziritas, G.[Georgios], Paragios, N.[Nikos],
Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies,
CVIU(112), No. 1, October 2008, pp. 14-29.
Elsevier DOI 0810
BibRef
Earlier:
Fast, Approximately Optimal Solutions for Single and Dynamic MRFs,
CVPR07(1-8).
IEEE DOI
PDF File. 0706
Code, Alignment.
WWW Link. BibRef
Earlier: A1, A3, A2:
MRF Optimization via Dual Decomposition: Message-Passing Revisited,
ICCV07(1-8).
IEEE DOI 0710
Nonlinear programming techniques. Markov random fields; Linear programming; Primal-dual schema; Discrete optimization; Graph cuts BibRef

Komodakis, N.[Nikos], Paragios, N.[Nikos], Tziritas, G.[Georgios],
MRF Energy Minimization and Beyond via Dual Decomposition,
PAMI(33), No. 3, March 2011, pp. 531-552.
IEEE DOI 1102
New framework for MRF optimization. First decompose into subproblems, then combine solutions. BibRef

Komodakis, N.[Nikos],
Efficient training for pairwise or higher order CRFs via dual decomposition,
CVPR11(1841-1848).
IEEE DOI 1106
BibRef

Komodakis, N.[Nikos],
Learning to cluster using high order graphical models with latent variables,
ICCV11(73-80).
IEEE DOI 1201
BibRef

Komodakis, N.[Nikos], Paragios, N.[Nikos],
Beyond pairwise energies: Efficient optimization for higher-order MRFs,
CVPR09(2985-2992).
IEEE DOI 0906
BibRef
Earlier:
Beyond Loose LP-Relaxations: Optimizing MRFs by Repairing Cycles,
ECCV08(III: 806-820).
Springer DOI 0810
BibRef

Komodakis, N.[Nikos],
Towards More Efficient and Effective LP-Based Algorithms for MRF Optimization,
ECCV10(II: 520-534).
Springer DOI 1009
BibRef

Nowozin, S.[Sebastian], Lampert, C.H.[Christoph H.],
Global Interactions In Random Field Models: A Potential Function Ensuring Connectedness,
SIIMS(3), No. 4, 2010, pp. 1048-1074.
WWW Link.
DOI Link BibRef 1000
Earlier:
Global connectivity potentials for random field models,
CVPR09(818-825).
IEEE DOI 0906
Markov random fields; potential functions; large cliques; high-arity interactions BibRef

Levada, A.L.M.[Alexandre L.M.], Mascarenhas, N.D.A.[Nelson D.A.], Tannus, A.[Alberto],
A novel MAP-MRF approach for multispectral image contextual classification using combination of suboptimal iterative algorithms,
PRL(31), No. 13, 1 October 2010, pp. 1795-1808.
Elsevier DOI 1003
BibRef
Earlier:
On the asymptotic variances of Gaussian Markov Random Field model hyperparameters in stochastic image modeling,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And:
A novel pseudo-likelihood equation for Potts MRF model parameter estimation in image analysis,
ICIP08(1828-1831).
IEEE DOI 0810
BibRef
And:
Improving Potts MRF model parameter estimation using higher-order neighborhood systems on stochastic image modeling,
WSSIP08(385-388).
IEEE DOI 0806
Contextual classification; Markov random fields; Combinatorial optimization; Maximum pseudo-likelihood; Data fusion; Classifier combination BibRef

Kim, W.S.[Won-Sik], Lee, K.M.[Kyoung Mu],
A hybrid approach for MRF optimization problems: Combination of stochastic sampling and deterministic algorithms,
CVIU(115), No. 12, December 2011, pp. 1623-1637.
Elsevier DOI 1111
BibRef
Earlier:
Continuous Markov Random Field Optimization Using Fusion Move Driven Markov Chain Monte Carlo Technique,
ICPR10(1364-1367).
IEEE DOI 1008
BibRef
Earlier:
Markov Chain Monte Carlo combined with deterministic methods for Markov random field optimization,
CVPR09(1406-1413).
IEEE DOI 0906
Analysis of the issues and techniques for computation methods in energy minimization. Markov Chain Monte Carlo; Markov Random Field model; Energy minimization; Optimization BibRef

Kim, W.S.[Won-Sik], Lee, K.M.[Kyoung Mu],
MRF optimization by graph approximation,
CVPR15(1063-1071)
IEEE DOI 1510
BibRef

Kim, W.S.[Won-Sik], Lee, K.M.[Kyoung Mu],
Scanline Sampler without Detailed Balance: An Efficient MCMC for MRF Optimization,
CVPR14(1354-1361)
IEEE DOI 1409
MCMC; MRF optimization; sampler; scanline BibRef

Lai, M.J.[Ming-Jun], Yin, W.T.[Wo-Tao],
Augmented L_1 and Nuclear-Norm Models with a Globally Linearly Convergent Algorithm,
SIIMS(6), No. 2, 2013, pp. 1059-1091.
DOI Link 1307
BibRef

Fritsche, C., Orguner, U., Svensson, L., Gustafsson, F.,
The Marginal Enumeration Bayesian Cramer-Rao Bound for Jump Markov Systems,
SPLetters(21), No. 4, April 2014, pp. 464-468.
IEEE DOI 1403
Approximation methods BibRef

Fritsche, C., Gustafsson, F.,
The Marginal Bayesian Cramer-Rao Bound for Jump Markov Systems,
SPLetters(23), No. 5, May 2016, pp. 575-579.
IEEE DOI 1604
Bayes methods BibRef

Jiang, X.Y.[Xin-Yang], Wu, F.[Fei], Zhang, Y.[Yin], Tang, S.L.[Si-Liang], Lu, W.M.[Wei-Ming], Zhuang, Y.T.[Yue-Ting],
The classification of multi-modal data with hidden conditional random field,
PRL(51), No. 1, 2015, pp. 63-69.
Elsevier DOI 1412
Hidden conditional random field M-HCRF. BibRef

Lu, C.Y.[Can-Yi], Lin, Z.C.[Zhou-Chen], Yan, S.C.[Shui-Cheng],
Smoothed Low Rank and Sparse Matrix Recovery by Iteratively Reweighted Least Squares Minimization,
IP(24), No. 2, February 2015, pp. 646-654.
IEEE DOI 1502
iterative methods BibRef

Lu, C.Y.[Can-Yi], Tang, J.H.[Jin-Hui], Yan, S.C.[Shui-Cheng], Lin, Z.C.[Zhou-Chen],
Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm,
IP(25), No. 2, February 2016, pp. 829-839.
IEEE DOI 1601
BibRef
Earlier:
Generalized Nonconvex Nonsmooth Low-Rank Minimization,
CVPR14(4130-4137)
IEEE DOI 1409
Approximation methods BibRef

Lu, P.[Peng], Peng, X.J.[Xu-Jun], Zhu, X.S.[Xin-Shan], Li, R.F.[Rui-Fan],
Finding more relevance: Propagating similarity on Markov random field for object retrieval,
SP:IC(32), No. 1, 2015, pp. 54-68.
Elsevier DOI 1503
Image retrieval BibRef

Song, D.J.[Dong-Jin], Liu, W.[Wei], Zhou, T.Y.[Tian-Yi], Tao, D.C.[Da-Cheng], Meyer, D.A.,
Efficient Robust Conditional Random Fields,
IP(24), No. 10, October 2015, pp. 3124-3136.
IEEE DOI 1507
convergence of numerical methods BibRef

Bousmalis, K., Zafeiriou, S.P., Morency, L., Pantic, M., Ghahramani, Z.,
Variational Infinite Hidden Conditional Random Fields,
PAMI(37), No. 9, September 2015, pp. 1917-1929.
IEEE DOI 1508
Analytical models BibRef

Kappes, J.H.[Jörg Hendrik], Speth, M.[Markus], Reinelt, G.[Gerhard], Schnörr, C.[Christoph],
Higher-order segmentation via multicuts,
CVIU(143), No. 1, 2016, pp. 104-119.
Elsevier DOI 1601
BibRef
Earlier:
Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization,
CVPR13(1752-1758)
IEEE DOI 1309
Segmentation. Graphical models; Markov random fields; discrete optimization BibRef

Zivkovic, Z.[Zoran],
Gentle ICM energy minimization for Markov random fields with smoothness-based priors,
RealTimeIP(11), No. 1, January 2016, pp. 235-246.
WWW Link. 1601
BibRef

Khandelwal, D.[Dinesh], Bhatia, K.[Kush], Arora, C.[Chetan], Singla, P.[Parag],
Lazy Generic Cuts,
CVIU(143), No. 1, 2016, pp. 80-91.
Elsevier DOI 1601
Markov random fields. efficient algorithm for inference in binary higher order MRF-MAP. BibRef

Ha, J.[Jeongmok], Jeong, H.[Hong],
A fast scanning based message receiving method on four directed acyclic subgraphs,
JVCIR(38), No. 1, 2016, pp. 161-174.
Elsevier DOI 1605
Markov random fields MAP inference method. BibRef

Nguyen, L.V., Kodagoda, S., Ranasinghe, R.,
Spatial Sensor Selection via Gaussian Markov Random Fields,
SMCS(46), No. 9, September 2016, pp. 1226-1239.
IEEE DOI 1609
Computational modeling BibRef

Hovhannisyan, V.[Vahan], Parpas, P.[Panos], Zafeiriou, S.P.[Stefanos P.],
MAGMA: Multilevel Accelerated Gradient Mirror Descent Algorithm for Large-Scale Convex Composite Minimization,
SIIMS(9), No. 4, 2016, pp. 1829-1857.
DOI Link 1612
BibRef

Luong, D.V.N.[Duy V. N.], Parpas, P.[Panos], Rueckert, D.[Daniel], Rustem, B.[Berç],
Solving MRF Minimization by Mirror Descent,
ISVC12(I: 587-598).
Springer DOI 1209
BibRef

França, G.[Guilherme], Bento, J.[José],
Markov Chain Lifting and Distributed ADMM,
SPLetters(24), No. 3, March 2017, pp. 294-298.
IEEE DOI 1702
Computer science BibRef

Llorente, F., Martino, L., Delgado, D.,
Parallel Metropolis-Hastings Coupler,
SPLetters(26), No. 6, June 2019, pp. 953-957.
IEEE DOI 1906
Sociology, Proposals, Couplers, TV, Kernel, Monte Carlo methods, Bayesian inference, MCMC algorithms, normal kernel coupler, population MCMC BibRef

Huang, X., Xu, S., Zhang, C., Zhang, J.,
Robust CP Tensor Factorization With Skew Noise,
SPLetters(27), 2020, pp. 785-789.
IEEE DOI 2006
Tensor factorization, mixture of asymmetric Laplacians, expectation-maximization (EM) algorithm BibRef

Yang, X.[Xu], Liu, Z.Y.[Zhi-Yong],
A Doubly Graduated Method for Inference in Markov Random Field,
SIIMS(14), No. 3, 2021, pp. 1354-1373.
DOI Link 2110
BibRef

Domokos, C.[Csaba], Schmidt, F.R.[Frank R.], Cremers, D.[Daniel],
MRF Optimization with Separable Convex Prior on Partially Ordered Labels,
ECCV18(VIII: 341-356).
Springer DOI 1810
BibRef

Khandelwal, D., Singla, P., Arora, C.,
Learning Higher Order Potentials for MRFs,
WACV18(812-820)
IEEE DOI 1806
Markov processes, gradient methods, inference mechanisms, learning (artificial intelligence), Training BibRef

Xie, J.W.[Jian-Wen], Xu, Y.F.[Yi-Fei], Nijkamp, E.[Erik], Wu, Y.N.[Ying Nian], Zhu, S.C.[Song-Chun],
Generative Hierarchical Learning of Sparse FRAME Models,
CVPR17(1933-1941)
IEEE DOI 1711
FRAME: Filters, Random field, And Maximum Entropy. Computational modeling, Deformable models, Mathematical model, Stochastic processes, Strain, Training, Visualization BibRef

Pagnozzi, A.M., Dowson, N., Bradley, A.P., Boyd, R.N., Bourgeat, P., Rose, S.,
Expectation-Maximization with Image-Weighted Markov Random Fields to Handle Severe Pathology,
DICTA15(1-6)
IEEE DOI 1603
Markov processes BibRef

Meir, O.[Omer], Galun, M.[Meirav], Yagev, S.[Stav], Basri, R.[Ronen], Yavneh, I.[Irad],
A Multiscale Variable-Grouping Framework for MRF Energy Minimization,
ICCV15(1805-1813)
IEEE DOI 1602
Crosstalk BibRef

Li, Y., Min, D., Brown, M.S., Do, M.N., Lu, J.,
SPM-BP: Sped-Up PatchMatch Belief Propagation for Continuous MRFs,
ICCV15(4006-4014)
IEEE DOI 1602
Adaptive optics BibRef

Ahmed, F., Tarlow, D., Batra, D.,
Optimizing Expected Intersection-Over-Union with Candidate-Constrained CRFs,
ICCV15(1850-1858)
IEEE DOI 1602
Bayes methods BibRef

Ha, J.M.[Jeong Mok], Jeon, B.[Byeongchan], Jeon, J.[Jea_Young], Jo, S.Y.[Sung Yong], Jeong, H.[Hong],
Cost aggregation table: A theoretic derivation on the Markov random field and its relation to message passing,
ICIP15(2224-2228)
IEEE DOI 1512
Markov random field BibRef

Sun, Q.[Qing], Laddha, A.[Ankit], Batra, D.[Dhruv],
Active learning for structured probabilistic models with histogram approximation,
CVPR15(3612-3621)
IEEE DOI 1510
e.g. Conditional Random Fields. BibRef

Wang, Z.H.[Zhen-Hua], Zhang, Z.Y.[Zhi-Yi], Geng, N.[Nan],
A Message Passing Algorithm for MRF Inference with Unknown Graphs and Its Applications,
ACCV14(IV: 288-302).
Springer DOI 1504
BibRef

Wang, J.Y.[Jun-Yan], Yeung, S.K.[Sai-Kit],
A Compact Linear Programming Relaxation for Binary Sub-modular MRF,
EMMCVPR15(29-42).
Springer DOI 1504
BibRef

Husain, F.[Farzad], Dellen, L.[Labette], Torras, C.[Carme],
Recognizing Point Clouds Using Conditional Random Fields,
ICPR14(4257-4262)
IEEE DOI 1412
Graphical models BibRef

Drory, A.[Amnon], Haubold, C.[Carsten], Avidan, S.[Shai], Hamprecht, F.A.[Fred A.],
Semi-Global Matching: A Principled Derivation in Terms of Message Passing,
GCPR14(43-53).
Springer DOI 1411
Derived from dense stereo, method to minimize the energy of a pairwise multi-label Markov Random Field. BibRef

Shekhovtsov, A.[Alexander],
Maximum Persistency in Energy Minimization,
CVPR14(1162-1169)
IEEE DOI 1409
energy minimization; max-sum; partial optimality; persistency; wcsp BibRef

Savchynskyy, B., Schmidt, S.,
Getting Feasible Variable Estimates from Infeasible Ones: MRF Local Polytope Study,
PGMs13(267-274)
IEEE DOI 1403
Markov processes BibRef

Turmukhambetov, D.[Daniyar], Campbell, N.D.F.[Neill D.F.], Prince, S.J.D.[Simon J.D.], Kautz, J.[Jan],
Modeling object appearance using Context-Conditioned Component Analysis,
CVPR15(4156-4164)
IEEE DOI 1510
BibRef

Campbell, N.D.F.[Neill D.F.], Subr, K.[Kartic], Kautz, J.[Jan],
Fully-Connected CRFs with Non-Parametric Pairwise Potential,
CVPR13(1658-1665)
IEEE DOI 1309
Conditional Random Fields: CRF; machine learning; non-parametric BibRef

Saito, M.[Masaki], Okatani, T.[Takayuki], Deguchi, K.[Koichiro],
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space,
CVPR13(57-64)
IEEE DOI 1309
Markov Random Fields BibRef

Nasihatkon, B., Hartley, R.I.,
Move-Based Algorithms for the Optimization of an Isotropic Gradient MRF Model,
DICTA12(1-8).
IEEE DOI 1303
BibRef

Schelten, K.[Kevin], Roth, S.[Stefan],
Mean Field for Continuous High-order MRFs,
DAGM12(52-61).
Springer DOI 1209
BibRef

Saito, M.[Masaki], Okatani, T.[Takayuki], Deguchi, K.[Koichiro],
Application of the mean field methods to MRF optimization in computer vision,
CVPR12(1680-1687).
IEEE DOI 1208
BibRef

Zheng, Y.[Yun], Chen, P.[Pei], Cao, J.Z.[Jiang-Zhong],
MAP-MRF inference based on extended junction tree representation,
CVPR12(1696-1703).
IEEE DOI 1208
BibRef

Vineet, V.[Vibhav], Warrell, J.[Jonathan], Torr, P.H.S.[Philip H.S.],
A tiered move-making algorithm for general pairwise MRFs,
CVPR12(1632-1639).
IEEE DOI 1208
BibRef

Kwon, D.J.[Dong-Jin], Lee, K.J.[Kyong Joon], Yun, I.D.[Il Dong], Lee, S.U.[Sang Uk],
Solving MRFs with Higher-Order Smoothness Priors Using Hierarchical Gradient Nodes,
ACCV10(I: 121-134).
Springer DOI 1011
BibRef

Gallagher, A.C.[Andrew C.], Batra, D.[Dhruv], Parikh, D.[Devi],
Inference for order reduction in Markov random fields,
CVPR11(1857-1864).
IEEE DOI 1106
BibRef

Batra, D.[Dhruv], Gallagher, A.C., Parikh, D.[Devi], Chen, T.H.[Tsu-Han],
Beyond trees: MRF inference via outer-planar decomposition,
CVPR10(2496-2503).
IEEE DOI 1006
unify approximate methods for Maximum a posteriori (MAP) inference in Markov Random Fields. BibRef

Zach, C.[Christopher], Niethammer, M.[Marc], Frahm, J.M.[Jan-Michael],
Continuous maximal flows and Wulff shapes: Application to MRFs,
CVPR09(1911-1918).
IEEE DOI 0906
Extend the continuous, isotropic maximal flow framework to the anisotropic case. BibRef

Ali, A.M.[Asem M.], Farag, A.A.[Aly A.], Gimel'farb, G.L.[Georgy L.],
Optimizing Binary MRFs with Higher Order Cliques,
ECCV08(III: 98-111).
Springer DOI 0810
Analysis of MRFs to use pairwise results at higher orders. Energy minimization. BibRef

Datta, R.[Ritendra], Hu, J.Y.[Jian-Ying], Ray, B.[Bonnie],
On efficient Viterbi decoding for hidden semi-Markov models,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Szummer, M.[Martin], Kohli, P.[Pushmeet], Hoiem, D.[Derek],
Learning CRFs Using Graph Cuts,
ECCV08(II: 582-595).
Springer DOI 0810
BibRef

Tappen, M.F.[Marshall F.],
Utilizing Variational Optimization to Learn Markov Random Fields,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Rother, C.[Carsten], Kolmogorov, V.[Vladimir], Lempitsky, V.[Victor], Szummer, M.[Martin],
Optimizing Binary MRFs via Extended Roof Duality,
CVPR07(1-8).
IEEE DOI 0706
BibRef

Tiwari, S., Gallager, S.,
Machine learning and multiscale methods in the identification of bivalve larvae,
ICCV03(494-500).
IEEE DOI 0311
BibRef
And:
Optimizing multiscale texture invariants for the identification of bivalve larvae,
ICIP03(III: 1061-1064).
IEEE DOI 0312
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Hidden Markov Models, General Problems, Computation, Use .