Dual decomposition solvers for the quadratic assignment problem (QAP), also called graph matching in computer vision, based on the LP_MP library.
- Minimum cost flow based solver .
- Pure message passing based solver .
- SRMP with the QAP reformulated as a graphical model .
- Hungarian belief propagation .
We use the input format of the solver , see here for the definition of this format.
git clone https://github.com/pawelswoboda/LP_MP-QAP.git to download the main repository and
git submodule update --init --remote --recursive the dependencies. Then type
cmake . to configure and
make to build.
Swoboda, P., Rother, C., Abu Alhaija, H., Kainmuller, D. and Savchynskyy, B. A Study of Lagrangean Decompositions and Dual Ascent Solvers for Graph Matching. CVPR, July 2017.
V. Kolmogorov. A New Look at Reweighted Message Passing. IEEE Trans. Pattern Anal. Mach. Intell., 37(5):919–930, 2015.
Zhang, Z. , Shi, Q. , McAuley, J. , Wei, W. , Zhang, Y. and van den Hengel, A. Pairwise Matching Through Max-Weight Bipartite Belief Propagation. CVPR, June 2016.
Torresani, L., Kolmogorov, V. and Rother, C. A Dual Decomposition Approach to Feature Correspondence. IEEE Trans. Pattern Anal. Mach. Intell., 35(2):259–271, 2013.