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  • Advances in Graph Matching for Image Interpretation

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    Dahm_2015_02Thesis.pdf (1.196Mb)
    Author(s)
    Dahm, Nicholas
    Primary Supervisor
    Gao, Yongsheng
    Other Supervisors
    Caelli, Terrence
    Bunke, Horst
    Year published
    2015
    Metadata
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    Abstract
    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 ...
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    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
    DOI
    https://doi.org/10.25904/1912/3323
    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
    Publication URI
    http://hdl.handle.net/10072/365647
    Collection
    • Theses - Higher Degree by Research

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