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Advances in Graph Matching for Image Interpretation

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Název: Advances in Graph Matching for Image Interpretation
Autoři: Dahm, Nicholas
Přispěvatelé: Gao, Yongsheng, Caelli, Terrence, Bunke, Horst
Informace o vydavateli: Griffith University
Rok vydání: 2015
Sbírka: Griffith University: Griffith Research Online
Témata: Structural pattern recognition, Graph matching, Graph matching algorithms, Image interpretation
Popis: 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. ; Thesis (PhD Doctorate) ; Doctor of Philosophy (PhD) ; Griffith School of Engineering ; Science, Environment, Engineering and Technology ; Full Text
Druh dokumentu: thesis
Jazyk: English
Relation: https://hdl.handle.net/10072/365647
DOI: 10.25904/1912/3323
Dostupnost: https://hdl.handle.net/10072/365647
https://doi.org/10.25904/1912/3323
Rights: The author owns the copyright in this thesis, unless stated otherwise. ; Public ; open access
Přístupové číslo: edsbas.B4A9B005
Databáze: BASE
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  – Url: https://hdl.handle.net/10072/365647#
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  Data: Advances in Graph Matching for Image Interpretation
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  Data: <searchLink fieldCode="AR" term="%22Dahm%2C+Nicholas%22">Dahm, Nicholas</searchLink>
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  Data: Gao, Yongsheng<br />Caelli, Terrence<br />Bunke, Horst
– Name: Publisher
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  Data: Griffith University
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  Data: 2015
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  Data: <searchLink fieldCode="DE" term="%22Structural+pattern+recognition%22">Structural pattern recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+matching%22">Graph matching</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+matching+algorithms%22">Graph matching algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+interpretation%22">Image interpretation</searchLink>
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  Label: Description
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  Data: 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. ; Thesis (PhD Doctorate) ; Doctor of Philosophy (PhD) ; Griffith School of Engineering ; Science, Environment, Engineering and Technology ; Full Text
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      – SubjectFull: Graph matching
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      – SubjectFull: Graph matching algorithms
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