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