Structural Pattern Recognition with Graph Edit Distance
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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.
Nákup knihy
Structural Pattern Recognition with Graph Edit Distance, Kaspar Riesen
- Jazyk
- Rok vydania
- 2018
Doručenie
Platobné metódy
2021 2022 2023
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- Titul
- Structural Pattern Recognition with Graph Edit Distance
- Jazyk
- anglicky
- Autori
- Kaspar Riesen
- Vydavateľ
- Springer
- Rok vydania
- 2018
- ISBN10
- 3319801015
- ISBN13
- 9783319801018
- Séria
- Advances in Computer Vision and Pattern Recognition
- Kategórie
- Počítače, IT, programovanie
- Anotácia
- 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.