DGM is a C++ library implementing various tasks in probabilistic graphical models with pairwise dependencies. The library aims to be used for the Markov and Conditional Random Fields (MRF / CRF), Markov Chains, Bayesian Networks, etc. Specifically, it includes a variety of methods for the following tasks:
- Learning: Training of unary and pairwise potentials
- Inference / Decoding: Computing the conditional probabilities and the most likely configuration
- Parameter Estimation: Computing maximum likelihood (or MAP) estimates of the parameters
- Evaluation / Visualization: Evaluation and visualization of the classification results
- Data Analysis: Extraction, analysis and visualization of valuable knowlage from training data
- Feature Extraction: Extraction of various descriptors from images, which are useful for classification
These tasks are optimized for speed, i.e. high-efficient calculations. The code is written in optimized C++11, compiled with Microsoft Visual Studio and can take advantage of multi-core processing. DGM is released under a BSD license and hence it is free for both academic and commercial use.