Structural Pattern Recognition with Graph Edit Distance
Kaspar Riesen
€ 127.52
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Description for Structural Pattern Recognition with Graph Edit Distance
Hardback. Series: Advances in Computer Vision and Pattern Recognition. Num Pages: 158 pages, 4 black & white illustrations, 24 colour illustrations, 28 black & white tables, 4 colour. BIC Classification: UMB; UYQP. Category: (G) General (US: Trade). Dimension: 235 x 155 x 11. Weight in Grams: 426.
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 ... Read more
Show LessProduct Details
Format
Hardback
Publication date
2016
Publisher
Springer International Publishing AG Switzerland
Number of pages
158
Condition
New
Series
Advances in Computer Vision and Pattern Recognition
Number of Pages
158
Place of Publication
Cham, Switzerland
ISBN
9783319272511
SKU
V9783319272511
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Kaspar Riesen
Dr. Kaspar Riesen 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.
Reviews for Structural Pattern Recognition with Graph Edit Distance
“The book presents the use of graphs in the field of structural pattern recognition. … The book is written in a very accessible fashion. The author gives many examples presenting the notations and problems considered. The book is suitable for graduate students and is an ideal reference for researchers and professionals interested in graph edit distance and its applications in ... Read more