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Schematic of graph filtration

About

This is a code repository written in MATLAB for the simulation study presented in the paper:

Songdechakraiwut T., Shen L., Chung M.: Topological learning and its application to multimodal brain network integration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). pp. 166-176. Springer (2021)

The simulation study aims to evaluate discriminative performance of the proposed topological loss against four well-known graph matching algorithms including graduated assignment [1], spectral matching [2], integer projected fixed point method [3] and reweighted random walk matching [4]. In addition, the study also evaluates run time efficiency of each algorithm.

Quick start

  1. Prerequisite: install Statistics and Machine Learning Toolbox
  2. Run make to compile and link C++ source files used for the baseline algorithms
  3. Execute run_simulation and track_runtime scripts

Organization

Code is organized as follows.

  • ./lib directory contains source code for the baseline algorithms
  • ./src directory contains the main implementation for the simulation study
  • make.m is a makefile for C++ source files used for the baseline algorithms
  • run_simulation.m runs the statistic simulations
  • track_runtime.m performs a rigorous measurement of execution time for each algorithm

Contact

If you have any questions, please feel free to contact Tananun Songdechakraiwut ([email protected]).

Acknowledgements

We thank all the authors for their generosity of sharing code [1-5].

References

[1] Gold, S., Rangarajan, A.: A graduated assignment algorithm for graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 18(4) (1996)

[2] Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: International Conference on Computer Vision. pp. 1482–1489. IEEE (2005)

[3] Leordeanu, M., Hebert, M., Sukthankar, R.: An integer projected fixed point method for graph matching and map inference. In: Advances in Neural Information Processing Systems. pp. 1114–1122 (2009)

[4] Cho, M., Lee, J., Lee, K.M.: Reweighted random walks for graph matching. In: European Conference on Computer Vision. pp. 492–505. Springer (2010)

[5] Zhou, F., De la Torre, F.: Deformable graph matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2922-2929 (2013)