# A General Multi-Graph Matching Approach via Graduated Consistency-regularized Boosting

arXiv: https://arxiv.org/abs/1502.05840 · HTML: https://mlsanity.com/paper/1502.05840/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2015.

## Built on by (most cited first, 12 of 12)

- [Deep Model Fusion: A Survey](https://mlsanity.com/paper/2309.15698/) (2023)
- [Structured Prediction Problem Archive](https://mlsanity.com/paper/2202.03574/) (2022)
- [Kernelized multi-graph matching](https://mlsanity.com/paper/2210.05206/) (2022)
- [Applying graph matching techniques to enhance reuse of plant design information](https://mlsanity.com/paper/2103.12466/) (2021)
- [Learning Universe Model for Partial Matching Networks over Multiple Graphs](https://mlsanity.com/paper/2210.10374/) (2022)
- [Optimizing Incomplete, Large-Scale and Sparse Multi-Graph Matching in Bioimaging](https://mlsanity.com/paper/2406.18215/) (2024)
- [Multi-view Geometry: Correspondences Refinement Based on Algebraic Properties](https://mlsanity.com/paper/2205.01634/) (2022)
- [MIXER: Multiattribute, Multiway Fusion of Uncertain Pairwise Affinities](https://mlsanity.com/paper/2210.08360/) (2022)
- [Population-wise Labeling of Sulcal Graphs using Multi-graph Matching](https://mlsanity.com/paper/2301.13532/) (2023)
- [From a few Accurate 2D Correspondences to 3D Point Clouds](https://mlsanity.com/paper/2206.08749/) (2022)
- [M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering](https://mlsanity.com/paper/2310.18444/) (2023)
- [C-DOG: Multi-View Multi-instance Feature Association Using Connected δ-Overlap Graphs](https://mlsanity.com/paper/2507.14095/) (2025)

## Abstract (arXiv)

This paper addresses the problem of matching N weighted graphs referring to an identical object or category. More specifically, matching the common node correspondences among graphs. This multi-graph matching problem involves two ingredients affecting the overall accuracy: i) the local pairwise matching affinity score among graphs; ii) the global matching consistency that measures the uniqueness of the pairwise matching results by different chaining orders. Previous studies typically either enforce the matching consistency constraints in the beginning of iterative optimization, which may propagate matching error both over iterations and across graph pairs; or separate affinity optimizing and consistency regularization in two steps. This paper is motivated by the observation that matching consistency can serve as a regularizer in the affinity objective function when the function is biased due to noises or inappropriate modeling. We propose multi-graph matching methods to incorporate the two aspects by boosting the affinity score, meanwhile gradually infusing the consistency as a regularizer. Furthermore, we propose a node-wise consistency/affinity-driven mechanism to elicit the common inlier nodes out of the irrelevant outliers. Extensive results on both synthetic and public image datasets demonstrate the competency of the proposed algorithms.