Structural Pattern Recognition with Graph Edit Distance: Approximation Algorithms and Applications
Structural Pattern Recognition with Graph Edit Distance: Approximation Algorithms and Applications
Springer | Polymer Science | February 10, 2016 | ISBN-10: 3319272519 | 158 pages | pdf | 3.1 mb
Springer | Polymer Science | February 10, 2016 | ISBN-10: 3319272519 | 158 pages | pdf | 3.1 mb
Authors: Riesen, Kaspar
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
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 discussed in the book.
Number of Illustrations and Tables
4 illus., 24 in colour
Topics
Pattern Recognition
Data Structures
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