Structural Pattern Recognition with Graph Edit Distance

Kaspar Riesen

ISBN 10: 3319801015 / ISBN 13: 9783319801018

Published by Springer International Publishing, Springer Nature Switzerland Mär 2018, 2018

About this Item

Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 172 pp. Englisch.

Seller Inventory # 9783319801018

Bibliographic Details

• Title: Structural Pattern Recognition with Graph Edit Distance
• Publisher: Springer International Publishing, Springer Nature Switzerland Mär 2018
• Publication Date: 2018
• Language: English
• ISBN 10: 3319801015
• ISBN 13: 9783319801018
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

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.

About the Author

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.