Multi-attributed graph matching with multi-layer random walks

Authors

• Han-Mu Park
• Kuk-Jin Yoon

Abstract

This paper addresses the multi-attributed graph matching problem considering multiple attributes jointly while preserving the characteristics of each attribute. Since most of conventional graph matching algorithms integrate multiple attributes to construct a single attribute in an oversimplified way, the information from multiple attributes are not often fully exploited. In order to solve this problem, we propose a novel multi-layer graph structure that can preserve the particularities of each attribute in separated layers. Then, we also propose a multi-attributed graph matching algorithm based on the random walk centrality for the proposed multi-layer graph structure. We compare the proposed algorithm with other state-of-the-art graph matching algorithms based on the single-layer structure using synthetic and real datasets, and prove the superior performance of the proposed multi-layer graph structure and matching algorithm.

Paper

ECCV 2016 paper (pdf, 1.54 MB)

ECCV 2016 supplementary materials (pdf, 1.54 MB)

TIP 2018 paper (pdf, 14.4 MB)

Code

MLRWM_Release_v1.0.3 (MATLAB, 4.19 MB) (link)

Citation

[1] Han-Mu Park and Kuk-Jin Yoon, "Multi-attributed graph matching with multi-layer random walks, " Proc. of European Conference on Computer Vision (ECCV), 2016.

[2] Han-Mu Park and Kuk-Jin Yoon, “Multi-attributed graph matching with multi-layer graph structure and multi-layer random walks,” IEEE Transactions on Image Processing, 2018.