Structural pattern recognition with graph edit distance approximation algorithms and applications / [electronic resource] :

• 作者： Riesen, Kaspar.
• 其他作者：
  • SpringerLink (Online service)
• 其他題名：
  • Advances in computer vision and pattern recognition,
• 出版： Cham : Springer International Publishing :Imprint: Springer
• 叢書名： Advances in computer vision and pattern recognition,
• 主題： Pattern recognition systems , Computer vision , Computer science , Pattern Recognition , Data Structures.
• ISBN： 9783319272528 (electronic bk.) 、 9783319272511 (paper)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Part I: Foundations and Applications of Graph Edit Distance -- Introduction and Basic Concepts -- Graph Edit Distance -- Bipartite Graph Edit Distance -- Part II: Recent Developments and Research on Graph Edit Distance -- Improving the Distance Accuracy of Bipartite Graph Edit Distance -- Learning Exact Graph Edit Distance -- Speeding Up Bipartite Graph Edit Distance -- Conclusions and Future Work -- Appendix A: Experimental Evaluation of Sorted Beam Search -- Appendix B: Data Sets.
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• 摘要註: This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED), one of the most flexible graph distance models available. The book also provides a detailed review of a diverse selection of novel methods related to GED, and concludes by suggesting possible avenues for future research. Topics and features: Formally introduces the concept of GED, and highlights the basic properties of this graph matching paradigm Describes a reformulation of GED to a quadratic assignment problem Illustrates how the quadratic assignment problem of GED can be reduced to a linear sum assignment problem Reviews strategies for reducing both the overestimation of the true edit distance and the matching time in the approximation framework Examines the improvement demonstrated by the described algorithmic framework with respect to the distance accuracy and the matching time Includes appendices listing the datasets employed for the experimental evaluations discussed in the book Researchers and graduate students interested in the field of structural pattern recognition will find this focused work to be an essential reference on the latest developments in GED. Dr. Kaspar Riesen is a university lecturer of computer science in the Institute for Information Systems at the University of Applied Sciences and Arts Northwestern Switzerland, Olten, Switzerland.
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