Structured Prediction for Smoothed Labeling Tasks
v1.343
by Daniel Munoz
Description
------------
A library for training Max-Margin Markov Networks with (Robust) Pott's
potentials, over arbitrary-sized cliques, trained with either:
- vanilla parametric subgradients
- Projected Euclidean functional subgradients (boosting)
This is a (better) re-implementation of the algorithm presented in:
Contextual Classification with Functional Max-Margin Markov Networks,
D. Munoz, J. A. Bagnell, N. Vandapel, M. Hebert,
CVPR 2009
with some differences:
- correct gradient calculation over Robust Pott's potentials
- non-linear capabilities via OpenCV regression trees
(and easily wrappable to any other regressor you may have)
License
----------
Modified BSD. I am interested in your usage, please let me know!
Dependencies
------------
submodular_graphcut/
- Boost Graph Library for maxflow (to maintain BSD license)
- (Optional) Vladimir Kolmogorov's faster maxflow-v3.1
(NOTE: this is a *research-only* license)
m3n/
- submodular_graphcut for inference
- Eigen (v2) linear algebra library for linear regression
- (Optional) OpenCV for regression trees, see example.cc code
- (Optional) gomp library http://gcc.gnu.org/projects/gomp, see example.cc code
Usage
---------
See m3n/examples/example.cc
Run ./example in m3n/examples for provided sample data.
Compiling
----------
Currently uses the CMake build infrastructure, run "cmake ."
Adjust CMakeLists.txt for opencv, gomp or custom path.
Citation
---------
If you use this software in a publication, you should cite:
Learning:
[1] D. Munoz, J. A. Bagnell, N. Vandapel, and M. Hebert,
"Contextual Classification with Functional Max-Margin Markov Networks", CVPR 2009.
[2] N. Ratliff, D. Silver, and J. A. Bagnell,
"Learning to Search: Functional Gradient Techniques for Imitation Learning", Autonomous Robots 2009
[3] B. Taskar, C. Guestrin, and D. Koller, "Max-Margin Markov Networks", NIPS 2003
Robust Pott's high-order cliques:
[4] P. Kohli, L. Ladicky, and P. H. S. Torr,
"Robust Higher Order Potentials for Enforcing Label Consistency", IJCV 2009
Alpha-expansion:
[5] Y. Boykov, O. Veksler, and R. Zabih, "Fast Approximate Energy Minimization via Graph Cuts", PAMI 2001
Efficient maxflow:
[6] Y. Boykov, and V. Kolmogorov,
"An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision", PAMI 2004
[7] V. Kolmogorov and R. Zabih, "What Energy Functions can be Minimized via Graph Cuts?", PAMI 2004
Acknowledgements
----------------
- This was mostly written during a 2009 internship at Willow Garage
- Balint Cristian , for the CMake files