DGM lib

Semantic Image Segmentation
with
Conditional Random Fields

Introduction

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 knowledge 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.

Quick Links

Application Examples

  • Remote Sensing

    First example compares 3 different classificators, based on Bayesian network, MRF and CRF respectively. The input here is a color-infra-red image with resolution of 800×800 pixels. Its ground-truth is given by manually labelled map of the same resolution. The corresponding graphical model consists of 160×160 nodes, thus each node corresponds to a small image patch with size of 5×5 pixels. 3 resulting label maps were compared with the ground-truth and the overall classification accuracies (OA) are reported:

  • Microbiology

    The labeling of Environmental Microorganisms which help decomposing pollutants, plays a fundamental role for establishing sustainable ecosystem. We propose an environmental microorganism classification engine that can automatically analyze microscopic images using Sparse Coding (SC) and Conditional Random Fields (CRF). First, to effectively represent scarce training images, SC is used to convert each of raw image patches into a higher-level local feature by reconstructing it with a linear combination of pixel patterns (bases), which are mined from a huge number of image patches. Finally, our CRF model localizes and classifies EMs by considering the spatial relations among features.

  • Stereo

    In this example we apply MRF to the stereo correspondence problem. The disparity map between left and right images of a stereo pair is estimated in form of a class map, where every class represents a small range of disparity values. The following result was achieved by applying approximate tree reweighted inference algorithm and simple Potts smoothness term, thus, no training was needed.

    Please refer to Demo Stereo for the source code and more details (appear first in DGM v.1.5.1).

  • Medical Imagery

    This example demonstrates the application to the segmentation problem. The input here is an RGB-color image of a human eye. The DGM-based classifier has been trained using 22 similar images, segmented manually. First, the segmentation was performed using Random Forest (RF) approach only, after that it was supported with the CRF technique:

      

    Four different colors represent skin (yellow), sclera (red), iris (blue) and pupil (gray).

If you have applied the DGM library in your work and achieved some interesting results, please send them to me via sergey.kosov@project-10.de, so I could allocate them at this web-page. This will help us to spread and improve the library. We thank you in advance for your support.

Downloads

Version 1.5.1 (CMake)

Source

Download the source code: DGM-1.5.1.zip

Win32 Binary

Download the DGM library with precompiled Win32 vc14 binaries: DGM-1.5.1-Win32-x86.exe

Win64 Binary

Download the DGM library with precompiled x64 vc14 binaries: DGM-1.5.1-Win64-x64.exe

Version 1.4.2 (MSVS)

Source

Download the source code: DGM-1.4.2.zip

Win32 Binary

Download the DGM library with precompiled Win32 vc14 binaries: DGM-1.4.2-Win32-x86.exe

Win64 Binary

Download the DGM library with precompiled x64 vc14 binaries: DGM-1.4.2-Win64-x64.exe

Note By installing, copying, or otherwise using this software, you agree to be bound by the terms of its license. Read the license.

DGM in Publication

Selected main publications:

See also more publications, where the DGM library was used:

To reference DGM in a publication, please include the library name and a link to this website [BibTeX]. You may also want to include the library version, since we currently update the software.

Citations

If you use this software in a publication, please cite the work using the following information:

Sergey Kosov. Direct graphical models C++ library. http://research.project-10.de/dgm/, 2013.

or using the BibTeX file.