@article {Kappes2015b,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015a,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	isbn = {25164671.25},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {kappes-1014-bench-arxiv,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {CoRR},
	volume = {abs/1404.0533},
	year = {2014},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@conference {Lenzen-2013-ssvm,
	title = {Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities},
	booktitle = {Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013},
	volume = {54},
	year = {2013},
	pages = {371--398},
	publisher = {Springer},
	organization = {Springer},
	author = {Frank Lenzen and Florian Becker and Lellmann, Jan}
}
@article {Lenzen-et-al-13,
	title = {A class of quasi-variational inequalities for adaptive image denoising and decomposition},
	journal = {Computational Optimization and Applications},
	volume = {54},
	number = {2},
	year = {2013},
	pages = {371-398},
	publisher = {Springer Netherlands},
	issn = {0926-6003},
	url = {http://dx.doi.org/10.1007/s10589-012-9456-0},
	author = {Frank Lenzen and Florian Becker and Lellmann, Jan and Stefania Petra and Christoph Schn{\"o}rr}
}
@article {Breitenreicher2012,
	title = {COAL: a generic modelling and prototyping framework for convex optimization problems of variational image analysis},
	journal = {Optimization Methods and Software},
	volume = {28},
	number = {5},
	year = {2013},
	note = {Projectpage: http://sourceforge.net/projects/coalproject/},
	pages = {1081-1094},
	doi = {10.1080/10556788.2012.672571},
	url = {http://www.tandfonline.com/doi/abs/10.1080/10556788.2012.672571},
	author = {Breitenreicher, Dirk and Lellmann, Jan and Christoph Schn{\"o}rr}
}
@conference {Kappes-2013-benchmark,
	title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem},
	booktitle = {CVPR},
	year = {2013},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Lellmann, Jan and Komodakis, Nikos and Carsten Rother}
}
@article {lellmann_13_optimality,
	title = {Optimality Bounds for a Variational Relaxation of the Image Partitioning
Problem},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {47 (3)},
	year = {2013},
	note = {1},
	pages = {239-257},
	doi = {10.1007/s10851-012-0390-7},
	author = {Lellmann, Jan and Frank Lenzen and Christoph Schn{\"o}rr}
}
@article {Lellmann-et-al-12,
	title = {Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {47},
	number = {3},
	year = {2012},
	pages = {239-257},
	publisher = {Springer},
	issn = {0924-9907},
	doi = {10.1007/s10851-012-0390-7},
	author = {Lellmann, Jan and Frank Lenzen and Christoph Schn{\"o}rr}
}
@article {Lellmann-et-al-12,
	title = {Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {47},
	number = {3},
	year = {2012},
	pages = {239-257},
	publisher = {Springer},
	issn = {0924-9907},
	doi = {10.1007/s10851-012-0390-7},
	author = {Lellmann, Jan and Lenzen, Frank and Christoph Schn{\"o}rr}
}
@conference {Lenzen-et-al-11,
	title = {Variational Image Denoising with Adaptive Constraint Sets},
	booktitle = {LNCS},
	year = {2012},
	pages = {206-217},
	publisher = {Springer},
	organization = {Springer},
	author = {Frank Lenzen and Florian Becker and Lellmann, Jan and Stefania Petra and Christoph Schn{\"o}rr}
}
@conference {Lenzen-et-al-11,
	title = {Variational Image Denoising with Adaptive Constraint Sets},
	booktitle = {Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision 2011},
	year = {2012},
	pages = {206-217},
	publisher = {Springer},
	organization = {Springer},
	author = {Lenzen, Frank and Florian Becker and Lellmann, Jan and Petra, Stefania and Christoph Schn{\"o}rr}
}
@conference {Lellmann2011,
	title = {Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem},
	booktitle = {Energy Min. Meth. Comp. Vis. Patt. Recogn.},
	volume = {6819},
	year = {2011},
	pages = {132--146},
	publisher = {Springer},
	organization = {Springer},
	author = {Lellmann, Jan and Frank Lenzen and Christoph Schn{\"o}rr},
	editor = {Boykov, Y. and Kahl, F. and Schmidt, F. R. and Lempitsky, V. F.}
}
@conference {Lellmann2011,
	title = {Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem},
	booktitle = {Energy Min. Meth. Comp. Vis. Patt. Recogn.},
	series = {LNCS},
	volume = {6819},
	year = {2011},
	pages = {132{\textendash}146},
	publisher = {Springer},
	organization = {Springer},
	author = {Lellmann, Jan and Lenzen, Frank and Christoph Schn{\"o}rr},
	editor = {Boykov, Y. and Kahl, F. and Lempitsky, V. F. and Schmidt, F. R.}
}
@techreport {Lellmann2011b,
	title = {Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem},
	year = {2011},
	month = {Dec},
	institution = {IPA group, Heidelberg University},
	url = {http://arxiv.org/abs/1112.0974},
	author = {Lellmann, Jan and Lenzen, Frank and Christoph Schn{\"o}rr}
}
@article {Breitenreicher-Lellmann-Schnoerr2011,
	title = {Sparse Template-Based Variational Image Segmentation},
	journal = {Advances in Adaptive Data Analysis},
	volume = {3},
	year = {2011},
	pages = {149-166},
	author = {Breitenreicher, Dirk and Lellmann, Jan and Christoph Schn{\"o}rr}
}
@article {Breitenreicher-Lellmann-Schnoerr2011,
	title = {Sparse Template-Based Variational Image Segmentation},
	journal = {Advances in Adaptive Data Analysis},
	volume = {3},
	year = {2011},
	pages = {149-166},
	author = {Breitenreicher, Dirk and Lellmann, Jan and Christoph Schn{\"o}rr}
}
