Structural Pattern Recognition with Graph Edit Distance

Approximation Algorithms and Applications

Nonfiction, Computers, Advanced Computing, Engineering, Computer Vision, Programming, Data Modeling & Design, General Computing
Cover of the book Structural Pattern Recognition with Graph Edit Distance by Kaspar Riesen, Springer International Publishing
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Author: Kaspar Riesen ISBN: 9783319272528
Publisher: Springer International Publishing Publication: January 9, 2016
Imprint: Springer Language: English
Author: Kaspar Riesen
ISBN: 9783319272528
Publisher: Springer International Publishing
Publication: January 9, 2016
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

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