Detailansicht
Structural Pattern Recognition with Graph Edit Distance
eBook - Approximation Algorithms and Applications, Advances in Computer Vision and Pattern Recognition
ISBN/EAN: 9783319272528
Umbreit-Nr.: 3355211
Sprache:
Englisch
Umfang: 0 S., 3.14 MB
Format in cm:
Einband:
Keine Angabe
Erschienen am 09.01.2016
Auflage: 1/2016
E-Book
Format: PDF
DRM: Digitales Wasserzeichen
- Zusatztext
- <p>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.<br/></p>
- Kurztext
- 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.
- Autorenportrait
- <p><strong>Dr. Kaspar Riesen</strong> 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.<br/></p>