A unified formulation of a class of graph matching techniques
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Pages (from-to) | 223-234 |
Journal / Publication | Pattern Recognition |
Volume | 95 |
Online published | 20 Jun 2019 |
Publication status | Published - Nov 2019 |
Link(s)
Abstract
In this paper, we show that graph matching methods based on relaxation labeling, spectral graph theory and tensor theory have the same mathematical form by employing power iteration technique. Besides, the differences among these methods are also fully discussed and can be proven that distinctions have little impact on the final matching result. Moreover, we propose a fast compatibility building procedure to accelerate the preprocessing speed which is considered to be the main time consuming part of graph matching. Finally, several experiments are conducted to verify our findings.
Research Area(s)
- Graph matching, Relaxation labeling, Spectral graph theory, Tensor theory
Citation Format(s)
A unified formulation of a class of graph matching techniques. / Zhu, Yuan; Zhou, Jiufeng; Yan, Hong.
In: Pattern Recognition, Vol. 95, 11.2019, p. 223-234.
In: Pattern Recognition, Vol. 95, 11.2019, p. 223-234.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review