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Structural pattern recognition with graph edit distance [electronic resource] : approximation algorithms and applications

Structural pattern recognition with graph edit distance [electronic resource] : approximation algorithms and applications

Material type
E-Book(소장)
Personal Author
Riesen, Kaspar.
Title Statement
Structural pattern recognition with graph edit distance [electronic resource] : approximation algorithms and applications / Kaspar Riesen.
Publication, Distribution, etc
Cham :   Springer International Publishing :   Imprint: Springer,   2015.  
Physical Medium
1 online resource (xiii, 158 p.) : ill. (some col.).
Series Statement
Advances in computer vision and pattern recognition,2191-6586
ISBN
9783319272528
요약
This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED), one of the most flexible graph distance models available. 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 Researchers and graduate students interested in the field of structural pattern recognition will find this focused work to be an essential reference on the latest developments in GED. 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.
General Note
Title from e-Book title page.  
Content Notes
Part I: Foundations and Applications of Graph Edit Distance -- Introduction and Basic Concepts -- Graph Edit Distance -- Bipartite Graph Edit Distance -- Part II: Recent Developments and Research on Graph Edit Distance -- Improving the Distance Accuracy of Bipartite Graph Edit Distance -- Learning Exact Graph Edit Distance -- Speeding Up Bipartite Graph Edit Distance -- Conclusions and Future Work -- Appendix A: Experimental Evaluation of Sorted Beam Search -- Appendix B: Data Sets.
Bibliography, Etc. Note
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
Subject Added Entry-Topical Term
Computer science. Pattern recognition systems. Computer vision.
Short cut
URL
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007 cr
008 200728s2015 sz a ob 001 0 eng d
020 ▼a 9783319272528
040 ▼a 211009 ▼c 211009 ▼d 211009
050 4 ▼a Q337.5
082 0 4 ▼a 006.4 ▼2 23
084 ▼a 006.4 ▼2 DDCK
090 ▼a 006.4
100 1 ▼a Riesen, Kaspar.
245 1 0 ▼a Structural pattern recognition with graph edit distance ▼h [electronic resource] : ▼b approximation algorithms and applications / ▼c Kaspar Riesen.
260 ▼a Cham : ▼b Springer International Publishing : ▼b Imprint: Springer, ▼c 2015.
300 ▼a 1 online resource (xiii, 158 p.) : ▼b ill. (some col.).
490 1 ▼a Advances in computer vision and pattern recognition, ▼x 2191-6586
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Part I: Foundations and Applications of Graph Edit Distance -- Introduction and Basic Concepts -- Graph Edit Distance -- Bipartite Graph Edit Distance -- Part II: Recent Developments and Research on Graph Edit Distance -- Improving the Distance Accuracy of Bipartite Graph Edit Distance -- Learning Exact Graph Edit Distance -- Speeding Up Bipartite Graph Edit Distance -- Conclusions and Future Work -- Appendix A: Experimental Evaluation of Sorted Beam Search -- Appendix B: Data Sets.
520 ▼a This unique text/reference presents a thorough introduction to the field of structural pattern recognition, with a particular focus on graph edit distance (GED), one of the most flexible graph distance models available. 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 Researchers and graduate students interested in the field of structural pattern recognition will find this focused work to be an essential reference on the latest developments in GED. 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Computer science.
650 0 ▼a Pattern recognition systems.
650 0 ▼a Computer vision.
830 0 ▼a Advances in computer vision and pattern recognition.
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=http://dx.doi.org/10.1007/978-3-319-27252-8
945 ▼a KLPA
991 ▼a E-Book(소장)

Holdings Information

No. Location Call Number Accession No. Availability Due Date Make a Reservation Service
No. 1 Location Main Library/e-Book Collection/ Call Number CR 006.4 Accession No. E14028686 Availability Loan can not(reference room) Due Date Make a Reservation Service M

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