Multi-Attributed Graph Matching With Multi-Layer Graph Structure and Multi-Layer Random Walks

• Published in: IEEE transactions on image processing Vol. 27; no. 5; pp. 2314 - 2325
• Main Authors: Park, Han-Mu, Yoon, Kuk-Jin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Graph matching; Histograms; Image edge detection; Indexes; multi-layer structure; multiple attributes; Periodic structures; Probabilistic logic; Random walk; Random walk theory; Transforms
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2017.2779264

This paper addresses the multi-attributed graph matching problem, which considers multiple attributes jointly while preserving the characteristics of each attribute for graph matching. Since most of conventional graph matching algorithms integrate multiple attributes to construct a single unified attribute in an oversimplified manner, the information from multiple attributes is often not completely utilized. In order to solve this problem, we propose a novel multi-layer graph structure that can preserve the characteristics of each attribute in separated layers, and also propose a multi-attributed graph matching algorithm based on random walk centrality with the proposed multi-layer graph structure. We compare the proposed algorithm with other state-of-the-art graph matching algorithms based on a single-layer structure using synthetic and real data sets and demonstrate the superior performance of the proposed multi-layer graph structure and the multi-attributed graph matching algorithm.