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.
| ISBN: | 9783319272511 |
| Publication date: | 8th February 2016 |
| Author: | Kaspar Riesen |
| Publisher: | Springer an imprint of Springer International Publishing |
| Format: | Hardback |
| Pagination: | 158 pages |
| Series: | Advances in Computer Vision and Pattern Recognition |
| Genres: |
Pattern recognition Databases |
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.
Structural Pattern Recognition With Graph Edit Distance features in the following genres: Pattern recognition, Databases
Structural Pattern Recognition With Graph Edit Distance is available in Hardback
Structural Pattern Recognition With Graph Edit Distance was written by Kaspar Riesen and published by Springer an imprint of Springer International Publishing
Structural Pattern Recognition With Graph Edit Distance has 158 pages
Yes it is part of Advances in Computer Vision and Pattern Recognition series