An Algorithm for Finding the Most Similar Given Sized Subgraphs in Two Weighted Graphs
Yang X(杨旭)
Source PublicationIEEE Transactions on Neural Networks and Learning Systems
2017
Issue0Pages:0
Abstract
We propose a weighted common subgraph (WCS) matching algorithm to find the most similar subgraphs in two labeled weighted graphs. WCS matching, as a natural generalization of equal-sized graph matching and subgraph matching, has found wide applications in many
computer vision and machine learning tasks. In this brief, WCS matching is first formulated as a combinatorial optimization problem over the set of partial permutation matrices. Then, it is approximately solved by a recently proposed combinatorial optimization framework—graduated nonconvexity and concavity procedure. Experimental comparisons on both synthetic graphs and real-world images validate its robustness against noise level, problem size, outlier number, and edge density.
Other Abstract
KeywordGraph Matching
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15486
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Recommended Citation
GB/T 7714
Yang X. An Algorithm for Finding the Most Similar Given Sized Subgraphs in Two Weighted Graphs[J]. IEEE Transactions on Neural Networks and Learning Systems,2017(0):0.
APA Yang X.(2017).An Algorithm for Finding the Most Similar Given Sized Subgraphs in Two Weighted Graphs.IEEE Transactions on Neural Networks and Learning Systems(0),0.
MLA Yang X."An Algorithm for Finding the Most Similar Given Sized Subgraphs in Two Weighted Graphs".IEEE Transactions on Neural Networks and Learning Systems .0(2017):0.
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