Structural Pattern Recognition with Graph Edit Distance

Approximation Algorithms and Applications
1. Auflage 2015
ISBN: 978-3-319-27251-1
Verlag: Springer International Publishing

Buch, Englisch, 158 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 3849 g

Reihe: Advances in Computer Vision and Pattern Recognition

This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED). 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 discussedin the book.

Zielgruppe

Research

Autoren/Hrsg.

Riesen, Kaspar

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen

Inhaltsverzeichnis

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.

Über Autor(innen)

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.