@article {Kappes2016,
title = {Multicuts and Perturb \& MAP for Probabilistic Graph Clustering},
journal = {Journal of Mathematical Imaging and Vision},
volume = {56},
number = {2},
year = {2016},
month = {jan},
pages = {221{\textendash}237},
abstract = {We present a probabilistic graphical model formulation for the graph clustering problem. This enables us to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. We exploit recent progress on globally optimal MAP inference by integer programming and on perturbation-based approximations of the log-partition function, in order to sample clusterings and to estimate marginal distributions of node-pairs both more accurately and more efficiently than state-of-the-art methods. Our approach works for any graphically represented problem instance. This is demonstrated for image segmentation and social network cluster analysis. Our mathematical ansatz should be relevant also for other combinatorial problems.},
keywords = {Correlation clustering, graphical models, Multicut, Perturb and MAP},
issn = {15737683},
doi = {10.1007/s10851-016-0659-3},
url = {http://arxiv.org/abs/1601.02088},
author = {Kappes, Jorg Hendrik and Swoboda, Paul and Savchynskyy, Bogdan and Hazan, Tamir and Christoph Schn{\"o}rr}
}
@article {Swoboda2016,
title = {Partial Optimality by Pruning for MAP-Inference with General Graphical Models},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {38},
number = {7},
year = {2016},
month = {jul},
pages = {1370{\textendash}1382},
publisher = {IEEE Computer Society},
abstract = {We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. The method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method{\textquoteright}s runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.},
keywords = {energy minimization, Local polytope, MAP-inference, Markov random fields, partial optimality, persistency},
issn = {01628828},
doi = {10.1109/TPAMI.2015.2484327},
author = {Swoboda, Paul and Shekhovtsov, Alexander and Kappes, Jorg Hendrik and Christoph Schn{\"o}rr and Savchynskyy, Bogdan}
}
@conference {Kappes2015c,
title = {Probabilistic correlation clustering and image partitioning using perturbed Multicuts},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {9087},
year = {2015},
pages = {231{\textendash}242},
abstract = {We exploit recent progress on globally optimal MAP inference by integer programming and perturbation-based approximations of the log-partition function. This enables to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. Our approach works for any graphically represented problem instance of correlation clustering, which is demonstrated by an additional social network example.},
keywords = {Correlation clustering, Multicut, Perturb and MAP},
isbn = {9783319184609},
issn = {16113349},
doi = {10.1007/978-3-319-18461-6_19},
author = {Kappes, Jorg Hendrik and Swoboda, Paul and Savchynskyy, Bogdan and Hazan, Tamir and Christoph Schn{\"o}rr}
}
@conference {Kappes-2014,
title = {MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves},
booktitle = {International Workshop on Graphical Models in Computer Vision},
year = {2014},
author = {Kappes, Jorg Hendrik and Thorsten Beier and Christoph Schn{\"o}rr}
}
@conference {SavchynskyyNIPS2013,
title = {Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation},
booktitle = {NIPS},
year = {2013},
note = {Accepted},
author = {Savchynskyy, Bogdan and Kappes, Jorg Hendrik and Swoboda, Paul and Christoph Schn{\"o}rr}
}
@booklet {Kappes-2013-multicut,
title = {Higher-order Segmentation via Multicuts},
howpublished = {ArXiv e-prints},
year = {2013},
month = {May},
author = {Kappes, Jorg Hendrik and Speth, Markus and Reinelt, Gerhard and Christoph Schn{\"o}rr}
}