Software for Factorized Graph Matching

This page contains software and instructions for factorized graph matching (FGM)^[1]^[2]. In addition, we include the following state-of-the-arts methods as baselines:

The implementations of the above methods are taken from the authors' websites (The code of GA was also implemented in the code of SMAC). We appreciate all the authors for their generosity in sharing codes.

                    >> cd fgm
                    >> ls
                    make.m addPath.m demoToy.m demoHouse.m testToy.m testHouse.m README.md
                    data save src lib
                    >> make
                    >> addPath
                    >> demoHouse
                  

The package of fgm.zip contains following folders and files:

data:	This folder contains the CMU House image dataset.
save:	This folder contains the experimental results reported in the paper.
src:	This folder contains the main implementation of FGM as well as other baselines.
lib:	This folder contains some necessary library functions.
make.m:	Matlab makefile for C++ code.
addPath.m:	Adds the sub-directories into the path of Matlab.
demoToy.m:	A demo comparison of different graph matching methods on the synthetic dataset.
demoHouse.m:	A demo comparison of different matching methods on the CMU House image dataset.
testToy.m:	Testing the performance of different graph matching methods on the synthetic dataset. This is a similar function used for reporting (Fig. 4) the first experiment (Sec 5.1) in the CVPR 2012 paper^[2].
testHouse.m:	Testing the performance of different graph matching methods on the CMU House image dataset. This is the same function used for reporting (Fig. 4) the first experiment (Sec 5.1) in the CVPR 2013 paper^[1].

Compiler error "Unrecognized switch: -argcheck".

This might due to the fact that your Windows c++ compiler cannot recognize the argument "-argcheck". To fix it, you can modify make.m from line 22 as follows:

                cd 'src/asg/smac/graph_matching_SMAC/cpp';
                mex -largeArrayDims mex_ICM_graph_matching.cpp
                mex -largeArrayDims mex_classes2indexes.cpp
                mex -largeArrayDims mex_computeRowSum.cpp
                mex -largeArrayDims mex_find_next_nonzero.cpp
                mex -largeArrayDims mex_ind2rgb8.cpp
                mex -largeArrayDims mex_ind2sub.cpp
                mex -largeArrayDims mex_istril.cpp
                mex -largeArrayDims mex_matchingW2.cpp
                mex -largeArrayDims mex_normalizeColumns.cpp
                mex -largeArrayDims mex_normalizeMatchingW.cpp
                mex -largeArrayDims mex_projection_QR_symmetric.cpp
                mex -largeArrayDims mex_projection_affine_symmetric.cpp
                mex -largeArrayDims mex_w_times_x_symmetric_tril.cpp
                mex -largeArrayDims spmtimesd.cpp
                %compileDir;
                cd(path0);
              

Thanks Syshiei's for providing the solution.

Which functions are implemented in C++?

We provide several C++ codes under src/asg/fgm/matrix to perform matrix products between binary matrices in a more efficient way. For instance, the function multiGXH.cpp is used to more efficiently compute the matrix product, G^T * X * H, where G and H are two binary matrices.

2013-05-08:

Add the implementations of the FGM-D, SMAC, PM, GA, IPFP-U, IPFP-S and RRWM.

Update the demo file by including FGM-D and more baselines for synthetic data and CMU House Image dataset.

Add a testing file for reproducing the reported experimental result on matching 100 pairs of synthetic graphs.

Add a testing file for reproducing the reported experimental result on matching the CMU House Image dataset.
2012-05-12:

Add the implementation of FGM-U and SM.

Add a demo file for synthetic data.

Add a demo file for CMU House Image dataset.

[1]

Deformable Graph Matching
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013
F. Zhou and F. De la Torre
[2]

Factorized Graph Matching
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012
F. Zhou and F. De la Torre
[3]

A Spectral Technique for Correspondence Problems Using Pairwise Constraints
International Conference on Computer Vision (ICCV), 2005
M. Leordeanu and M. Hebert
[4]

Balanced Graph Matching
Advances in Neural Information Processing Systems (NIPS), 2006
T. Cour, P. Srinivasan and J. Shi
[5]

A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 1996
S. Gold and A. Rangarajan
[6]

Probabilistic Graph and Hypergraph Matching
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008
R. Zass and A. Shashua
[7]

An Integer Projected Fixed Point Method for Graph Matching and MAP Inference
Advances in Neural Information Processing Systems (NIPS), 2009
M. Leordeanu, M. Hebert and R. Sukthankar
[8]

Reweighted Random Walks for Graph Matching
European Conference on Computer Vision (ECCV), 2010
M. Cho, J. Lee and K. Lee

This software is free for use in research projects. If you publish results obtained using this software, please use this citation:

@article{ZhouD16, author = {Feng Zhou and Fernando {De la Torre}}, title = {Factorized Graph Matching}, journal = {Transactions on Pattern Analysis and Machine Intelligence (PAMI)}, year = {2016}, volume = {38}, number = {9}, pages = {1774-1789}, }

Contributing back bug fixes and improvements is polite and encouraged. If you have any question, feel free to contact Feng Zhou.

Software for Factorized Graph Matching

Introduction

Installation

Package Content

FAQs

Compiler error "Unrecognized switch: -argcheck".

Which functions are implemented in C++?

Change Log

References

Copyright