@unpublished{2928,
abstract = { This paper addresses the problem of approximate MAP-MRF inference in general graphical models. Following [36], we consider a family of linear programming relaxations of the problem where each relaxation is specified by a set of nested pairs of factors for which the marginalization constraint needs to be enforced. We develop a generalization of the TRW-S algorithm [9] for this problem, where we use a decomposition into junction chains, monotonic w.r.t. some ordering on the nodes. This generalizes the monotonic chains in [9] in a natural way. We also show how to deal with nested factors in an efficient way. Experiments show an improvement over min-sum diffusion, MPLP and subgradient ascent algorithms on a number of computer vision and natural language processing problems. },
author = {Kolmogorov, Vladimir and Schoenemann, Thomas},
booktitle = {arXiv},
pages = {16},
publisher = {ArXiv},
title = {{Generalized sequential tree-reweighted message passing}},
year = {2012},
}
@inproceedings{2930,
abstract = {In this paper we investigate k-submodular functions. This natural family of discrete functions includes submodular and bisubmodular functions as the special cases k = 1 and k = 2 respectively.
In particular we generalize the known Min-Max-Theorem for submodular and bisubmodular functions. This theorem asserts that the minimum of the (bi)submodular function can be found by solving a maximization problem over a (bi)submodular polyhedron. We define a k-submodular polyhedron, prove a Min-Max-Theorem for k-submodular functions, and give a greedy algorithm to construct the vertices of the polyhedron.
},
author = {Huber, Anna and Kolmogorov, Vladimir},
location = {Athens, Greece},
pages = {451 -- 462},
publisher = {Springer},
title = {{Towards minimizing k-submodular functions}},
doi = {10.1007/978-3-642-32147-4_40},
volume = {7422},
year = {2012},
}
@article{2931,
abstract = {In this paper, we present a new approach for establishing correspondences between sparse image features related by an unknown nonrigid mapping and corrupted by clutter and occlusion, such as points extracted from images of different instances of the same object category. We formulate this matching task as an energy minimization problem by defining an elaborate objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general an NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples, DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods.
},
author = {Torresani, Lorenzo and Kolmogorov, Vladimir and Rother, Carsten},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {2},
pages = {259 -- 271},
publisher = {IEEE},
title = {{A dual decomposition approach to feature correspondence}},
doi = {10.1109/TPAMI.2012.105},
volume = {35},
year = {2012},
}
@misc{5396,
abstract = {We consider the problem of inference in agraphical model with binary variables. While in theory it is arguably preferable to compute marginal probabilities, in practice researchers often use MAP inference due to the availability of efficient discrete optimization algorithms. We bridge the gap between the two approaches by introducing the Discrete Marginals technique in which approximate marginals are obtained by minimizing an objective function with unary and pair-wise terms over a discretized domain. This allows the use of techniques originally devel-oped for MAP-MRF inference and learning. We explore two ways to set up the objective function - by discretizing the Bethe free energy and by learning it from training data. Experimental results show that for certain types of graphs a learned function can out-perform the Bethe approximation. We also establish a link between the Bethe free energy and submodular functions.},
author = {Korc, Filip and Kolmogorov, Vladimir and Lampert, Christoph},
issn = {2664-1690},
pages = {13},
publisher = {IST Austria},
title = {{Approximating marginals using discrete energy minimization}},
doi = {10.15479/AT:IST-2012-0003},
year = {2012},
}
@article{3117,
abstract = {We consider the problem of minimizing a function represented as a sum of submodular terms. We assume each term allows an efficient computation of exchange capacities. This holds, for example, for terms depending on a small number of variables, or for certain cardinality-dependent terms. A naive application of submodular minimization algorithms would not exploit the existence of specialized exchange capacity subroutines for individual terms. To overcome this, we cast the problem as a submodular flow (SF) problem in an auxiliary graph in such a way that applying most existing SF algorithms would rely only on these subroutines. We then explore in more detail Iwata's capacity scaling approach for submodular flows (Iwata 1997 [19]). In particular, we show how to improve its complexity in the case when the function contains cardinality-dependent terms.},
author = {Kolmogorov, Vladimir},
journal = {Discrete Applied Mathematics},
number = {15},
pages = {2246 -- 2258},
publisher = {Elsevier},
title = {{Minimizing a sum of submodular functions}},
doi = {10.1016/j.dam.2012.05.025},
volume = {160},
year = {2012},
}
@inproceedings{3124,
abstract = {We consider the problem of inference in a graphical model with binary variables. While in theory it is arguably preferable to compute marginal probabilities, in practice researchers often use MAP inference due to the availability of efficient discrete optimization algorithms. We bridge the gap between the two approaches by introducing the Discrete Marginals technique in which approximate marginals are obtained by minimizing an objective function with unary and pairwise terms over a discretized domain. This allows the use of techniques originally developed for MAP-MRF inference and learning. We explore two ways to set up the objective function - by discretizing the Bethe free energy and by learning it from training data. Experimental results show that for certain types of graphs a learned function can outperform the Bethe approximation. We also establish a link between the Bethe free energy and submodular functions.
},
author = {Korc, Filip and Kolmogorov, Vladimir and Lampert, Christoph},
location = {Edinburgh, Scotland},
publisher = {ICML},
title = {{Approximating marginals using discrete energy minimization}},
year = {2012},
}
@article{3257,
abstract = {Consider a convex relaxation f̂ of a pseudo-Boolean function f. We say that the relaxation is totally half-integral if f̂(x) is a polyhedral function with half-integral extreme points x, and this property is preserved after adding an arbitrary combination of constraints of the form x i=x j, x i=1-x j, and x i=γ where γ∈{0,1,1/2} is a constant. A well-known example is the roof duality relaxation for quadratic pseudo-Boolean functions f. We argue that total half-integrality is a natural requirement for generalizations of roof duality to arbitrary pseudo-Boolean functions. Our contributions are as follows. First, we provide a complete characterization of totally half-integral relaxations f̂ by establishing a one-to-one correspondence with bisubmodular functions. Second, we give a new characterization of bisubmodular functions. Finally, we show some relationships between general totally half-integral relaxations and relaxations based on the roof duality. On the conceptual level, our results show that bisubmodular functions provide a natural generalization of the roof duality approach to higher-order terms. This can be viewed as a non-submodular analogue of the fact that submodular functions generalize the s-t minimum cut problem with non-negative weights to higher-order terms.},
author = {Kolmogorov, Vladimir},
journal = {Discrete Applied Mathematics},
number = {4-5},
pages = {416 -- 426},
publisher = {Elsevier},
title = {{Generalized roof duality and bisubmodular functions}},
doi = {10.1016/j.dam.2011.10.026},
volume = {160},
year = {2012},
}