In recent years, multi-graph matching has become a popular yet challenging task in graph theory. There exist two major problems in multi-graph matching, i.e., the cycle-consistency problem, and the high time and space complexity problem. On one hand, the pairwise-based multi-graph matching methods are of low time and space complexity, but in order to keep the cycle-consistency of the matching results, they need additional constraints. Besides, the accuracy of the pairwise-based multi-graph matching is highly dependent on the selected optimization algorithms. On the other hand, the tensor-based multi-graph matching methods can avoid the cycle-consistency problem, while their time and space complexity is extremely high. In this paper, we found the equivalence between the pairwise-based and the tensor-based multi-graph matching methods under some specific circumstances. Based on this finding, we proposed a new multi-graph matching method, which not only avoids the cycle-consistency problem, but also reduces the complexity. In addition, we further improved the proposed method by introducing a lossless factorization of the affinity matrix in the multi-graph matching methods. Synthetic and real data experiments demonstrate the superiority of our method.
Fulltext of this paper can be found here.
All codes of the experiments in the manuscript are included, and are written and tested in MATLAB 2022a:
- exp_synthetic_noise.m,
- exp_synthetic_outlier.m,
- exp_time_cost.m,
- exp_cmu_test.m,
- exp_pfwillow_test.m.
- exp_pascalvoc_test.m
Please extract all compressed files in ./data/ and run startup.m before running any code of the experiment.
The results are saved in ./results, where there are also plotting codes.
Anti-noise performance, where number of graphs ranges from 3 to 12.
Anti-outlier performance, where number of graphs ranges from 3 to 12.
@article{
ZHU2023109597,
title = {Factorized Multi-Graph Matching},
journal = {Pattern Recognition},
pages = {109597},
year = {2023},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2023.109597},
url = {https://www.sciencedirect.com/science/article/pii/S0031320323002984}
}