Advances in Graph Matching for Image Interpretation

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Author(s)
Primary Supervisor
Gao, Yongsheng
Other Supervisors
Caelli, Terrence
Bunke, Horst
Year published
2015
Metadata
Show full item recordAbstract
In structural pattern recognition, graphs are a powerful and flexible data structure, allowing for the description of complex relationships between data elements. This flexibility comes at a cost, as the unconstrained nature of graphs results in a high computational complexity for graph matching algorithms. Various algorithms have been proposed to mitigate this complexity and make graph matching tractable. Additionally, in domains where the number of graph nodes is low, or where the data provides additional constraints, such as node and edge labels, graph matching has been effectively applied. Such applications include ...
View more >In structural pattern recognition, graphs are a powerful and flexible data structure, allowing for the description of complex relationships between data elements. This flexibility comes at a cost, as the unconstrained nature of graphs results in a high computational complexity for graph matching algorithms. Various algorithms have been proposed to mitigate this complexity and make graph matching tractable. Additionally, in domains where the number of graph nodes is low, or where the data provides additional constraints, such as node and edge labels, graph matching has been effectively applied. Such applications include chemical structure matching, protein-protein interaction networks, and network analysis. In the domain of computer vision, graphs have been successfully applied to a number a problems including image segmentation, partitioning, and matching. However, for practical reasons, many of the image matching techniques that utilise graphs do not match graph topology directly. Instead, image graphs are used only to constrain feature locations, or spectral embedding is used to transform image graphs into vectors, which are then matched.
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View more >In structural pattern recognition, graphs are a powerful and flexible data structure, allowing for the description of complex relationships between data elements. This flexibility comes at a cost, as the unconstrained nature of graphs results in a high computational complexity for graph matching algorithms. Various algorithms have been proposed to mitigate this complexity and make graph matching tractable. Additionally, in domains where the number of graph nodes is low, or where the data provides additional constraints, such as node and edge labels, graph matching has been effectively applied. Such applications include chemical structure matching, protein-protein interaction networks, and network analysis. In the domain of computer vision, graphs have been successfully applied to a number a problems including image segmentation, partitioning, and matching. However, for practical reasons, many of the image matching techniques that utilise graphs do not match graph topology directly. Instead, image graphs are used only to constrain feature locations, or spectral embedding is used to transform image graphs into vectors, which are then matched.
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Thesis Type
Thesis (PhD Doctorate)
Degree Program
Doctor of Philosophy (PhD)
School
Griffith School of Engineering
Copyright Statement
The author owns the copyright in this thesis, unless stated otherwise.
Item Access Status
Public
Subject
Structural pattern recognition
Graph matching
Graph matching algorithms
Image interpretation