Structural Pattern Recognition with Graph Edit Distance

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.

Provides a thorough introduction to the concept of graph edit distance (GED) Describes a selection of diverse GED algorithms with step-by-step examples Presents a unique overview of recent pattern recognition applications based on GED Includes several novel and significant extensions of GED, with a special focus on fast approximation algorithms for GED Includes supplementary material: sn.pub/extras

Autorentext

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.

Inhalt

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.