S. Alchatzidis, A. Sotiras, and N. Paragios, Discrete Multi Atlas Segmentation using Agreement Constraints, Proceedings of the British Machine Vision Conference 2014, 2014.
DOI : 10.5244/C.28.20

URL : https://hal.archives-ouvertes.fr/hal-01061457

D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta et al., Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.169-176, 2005.
DOI : 10.1109/CVPR.2005.133

D. P. Bertsekas, Nonlinear Programming, Athena, 1999.

A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr, Interactive Image Segmentation Using an Adaptive GMMRF Model, ECCV, pp.428-441, 2004.
DOI : 10.1007/978-3-540-24670-1_33

B. Matthew, C. H. Blaschko, and . Lampert, Learning to localize objects with structured output regression, ECCV, 2008.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Machine Learning, pp.1-122, 2011.
DOI : 10.1561/2200000016

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

Y. Boykov, O. Veksler, and R. Zabih, Efficient approximate energy minimization via graph cuts. T-PAMI, pp.1222-1239, 2001.

D. Chakrabarty, P. Jain, and P. Kothari, Provable submodular minimization using Wolfe's algorithm, NIPS, 2014.

G. Charpiat, Exhaustive family of energies minimizable exactly by a graph cut, CVPR 2011, 2011.
DOI : 10.1109/CVPR.2011.5995567

URL : https://hal.archives-ouvertes.fr/inria-00616370

S. Fujishige, Lexicographically Optimal Base of a Polymatroid with Respect to a Weight Vector, Mathematics of Operations Research, vol.5, issue.2, pp.186-196, 1980.
DOI : 10.1287/moor.5.2.186

B. Fulkerson, A. Vedaldi, and S. Soatto, Class segmentation and object localization with superpixel neighborhoods, 2009 IEEE 12th International Conference on Computer Vision, 2009.
DOI : 10.1109/ICCV.2009.5459175

C. Varun-gulshan, A. Rother, A. Criminisi, A. Blake, and . Zisserman, Geodesic star convexity for interactive image segmentation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.3129-3136, 2010.
DOI : 10.1109/CVPR.2010.5540073

S. Iwata, A Faster Scaling Algorithm for Minimizing Submodular Functions, SIAM Journal on Computing, vol.32, issue.4, pp.833-840, 2003.
DOI : 10.1137/S0097539701397813

T. Joachims, T. Finley, and C. Yu, Cutting-plane training of structural SVMs, Machine Learning, pp.27-59, 2009.
DOI : 10.1007/s10994-009-5108-8

V. Kolmogorov, Minimizing a sum of submodular functions, Discrete Applied Mathematics, vol.160, issue.15, pp.2246-2258, 2012.
DOI : 10.1016/j.dam.2012.05.025

V. Kolmogorov and R. Zabin, What energy functions can be minimized via graph cuts? T-PAMI, pp.147-159, 2004.

N. Komodakis, N. Paragios, and G. Tziritas, MRF Optimization via Dual Decomposition: Message-Passing Revisited, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4408890

A. Krause, SFO: A toolbox for submodular function optimization, JMLR, vol.11, pp.1141-1144, 2010.

J. D. Lafferty, A. Mccallum, and F. C. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, ICML, 2001.

O. Meshi, N. Srebro, and T. Hazan, Efficient training of structured svms via soft constraints, AISTATS, pp.699-707, 2015.

R. Nishihara, S. Jegelka, and M. I. Jordan, On the convergence rate of decomposable submodular function minimization, NIPS, pp.640-648, 2014.

S. Nowozin, Optimal Decisions from Probabilistic Models: The Intersection-over-Union Case, 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014.
DOI : 10.1109/CVPR.2014.77

S. Nowozin and C. H. Lampert, Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision, pp.3-4, 2011.

J. B. Orlin, A faster strongly polynomial time algorithm for submodular function minimization, Mathematical Programming, pp.237-251, 2009.

A. Osokin and P. Kohli, Perceptually Inspired Layout-Aware Losses for Image Segmentation, ECCV, 2014.
DOI : 10.1007/978-3-319-10605-2_43

P. Pletscher and P. Kohli, Learning low-order models for enforcing highorder statistics, AISTATS, 2012.

M. Queyranne, Minimizing symmetric submodular functions, Mathematical Programming, vol.26, issue.2, pp.3-12, 1998.
DOI : 10.1007/BF01585863

T. Rohlfing, Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable, IEEE Transactions on Medical Imaging, vol.31, issue.2, pp.153-163, 2012.
DOI : 10.1109/TMI.2011.2163944

C. Rother, V. Kolmogorov, V. Lempitsky, and M. Szummer, Optimizing Binary MRFs via Extended Roof Duality, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383203

M. Schmidt, UGM: Matlab code for undirected graphical models, 2012.

A. Schrijver, Combinatorial Optimization: Polyhedra and Efficiency, 2004.

P. Stobbe and A. Krause, Efficient minimization of decomposable submodular functions, NIPS, 2010.

M. Szummer, P. Kohli, and D. Hoiem, Learning CRFs Using Graph Cuts, ECCV, pp.582-595, 2008.
DOI : 10.1007/978-3-540-88688-4_43

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.7402

D. Tarlow, S. Richard, and . Zemel, Structured output learning with high order loss functions, AISTATS, 2012.

D. Tarlow, I. E. Givoni, and R. S. Zemel, HOP-MAP: Efficient message passing with high order potentials, AISTATS, 2010.

B. Taskar, C. Guestrin, and D. Koller, Max-margin Markov networks, NIPS, 2003.

I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large margin methods for structured and interdependent output variables, JMLR, pp.1453-1484, 2005.

J. Yu and M. B. Blaschko, Learning submodular losses with the Lovász hinge, ICML, pp.1623-1631, 2015.

W. Zaremba and M. B. Blaschko, Discriminative training of CRF models with probably submodular constraints, 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.
DOI : 10.1109/WACV.2016.7477696