OpenGM is a C++ template library for discrete factor graph models and distributive operations on these models. It includes state-of-the-art optimization and inference algorithms beyond message passing. OpenGM handles large models efficiently, since (i) functions that occur repeatedly need to be stored only once and (ii) when functions require different parametric or non-parametric encodings, multiple encodings can be used alongside each other, in the same model, using included and custom C++ code. No restrictions are imposed on the factor graph or the operations of the model. OpenGM is modular and extendible. Elementary data types can be chosen to maximize efficiency. The graphical model data structure, inference algorithms and different encodings of functions inter-operate through well-defined interfaces. The binary OpenGM file format is based on the HDF5 standard and incorporates user extensions automatically.
Keywords for this software
References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Hühnerbein, Ruben; Savarino, Fabrizio; Åström, Freddie; Schnörr, Christoph: Image labeling based on graphical models using Wasserstein messages and geometric assignment (2018)
- Hurley, Barry; O’Sullivan, Barry; Allouche, David; Katsirelos, George; Schiex, Thomas; Zytnicki, Matthias; de Givry, Simon: Multi-language evaluation of exact solvers in graphical model discrete optimization (2016)
- Kappes, Jörg Hendrik; Swoboda, Paul; Savchynskyy, Bogdan; Hazan, Tamir; Schnörr, Christoph: Multicuts and perturb & MAP for probabilistic graph clustering (2016)
- Wang, Peng; Shen, Chunhua; van den Hengel, Anton; Torr, Philip H. S.: Efficient semidefinite branch-and-cut for MAP-MRF inference (2016)
- Kappes, Jörg Hendrik; Swoboda, Paul; Savchynskyy, Bogdan; Hazan, Tamir; Schnörr, Christoph: Probabilistic correlation clustering and image partitioning using perturbed multicuts (2015)
- Lowd, Daniel; Rooshenas, Amirmohammad: The Libra toolkit for probabilistic models (2015)
- Andres, Björn; Kappes, Jörg H.; Köthe, Ullrich; Schnörr, Christoph; Hamprecht, Fred A.: An empirical comparison of inference algorithms for graphical models with higher order factors using OpenGM (2010) ioport
Further publications can be found at: http://hciweb2.iwr.uni-heidelberg.de/opengm/index.php?l0=ref