OpenGM: A C++ Library for Discrete Graphical Models

By Björn Andres, Thorsten Beier, Jörg H. Kappes. 2012

105 citations recorded by the source corpus.

OpenGM is a C++ template library for defining discrete graphical models and performing inference on these models, using a wide range of state-of-the-art algorithms. No restrictions are imposed on the factor graph to allow for higher-order factors and arbitrary neighborhood structures. Large models with repetitive structure are handled efficiently because (i) functions that occur repeatedly need to be stored only once, and (ii) distinct functions can be implemented differently, using different encodings alongside each other in the same model. Several parametric functions (e.g. metrics), sparse and dense value tables are provided and so is an interface for custom C++ code. Algorithms are separated by design from the representation of graphical models and are easily exchangeable. OpenGM, its algorithms, HDF5 file format and command line tools are modular and extendible.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Probabilistic & Bayesian Methods, Signal Processing

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Citations in the corpus

Barrier Frank-Wolfe for Marginal Inference (2015, methodology): “Our marginals are visually similar to those obtained by TRBP and our algorithm is able to scale to large instances by using approximate MAP solvers.”
Efficient Decomposition of Image and Mesh Graphs by Lifted Multicuts (2015, neutral): “Our implementation of (an extension of) the Kernighan-Lin Algorithm (KL) [29] is compared here, in addition, to the implementation of KL in [4, 25].”
Cut, Glue, & Cut: A Fast, Approximate Solver for Multicut Partitioning (2014, methodology): “(v) Our C++ implementation of CGC is part of the open-source OpenGM inference library [3].”
Image retrieval using scene graphs (2015, methodology): “We perform approximate inference using off-the-shelf belief propagation [3].”
Asymmetric Cuts: Joint Image Labeling and Partitioning (2014, methodology): “The C++-code of our method will be made available within OpenGM [2].”
Greedy Direction Method of Multiplier for MAP Inference of Large Output Domain (2017, methodology): “We used implementation from the OPENGM Library [28, 29], which wraps the original TRWS code.”
Hand Pose Estimation through Weakly-Supervised Learning of a Rich Intermediate Representation (2015, methodology): “Inference was performed through message passing using the open-GM library [1], and the hyper-parameter α was opti-”
Advanced Structured Prediction (2014, methodology): “…enables the construction of adaptive optimization schemes depending on the duality gap, for example adaptive step-size selection in subgradient-based schemes (Ko-modakis et al., 2011; Kappes et al., 2012) or adaptive smoothing selection procedures for non-smooth problems (Savchynskyy et al., 2012).”