Graph matching : fast candidate elimination using machine learning techniques

Lazarescu, M., Bunke, H. and Venkatesh, S. 2000, Graph matching : fast candidate elimination using machine learning techniques, in Advances in Pattern Recognition : Joint IAPR International Workshops SSPR 2000 and SPR 2000, Alicante, Spain, August 30 – September 1, 2000 proceedings, Springer, Berlin, Germany, pp. 236-245.

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Title Graph matching : fast candidate elimination using machine learning techniques
Author(s) Lazarescu, M.
Bunke, H.
Venkatesh, S.
Conference name Joint IAPR International Workshops SSPR and SPR (2000 : Alicante, Spain)
Conference location Alicante, Spain
Conference dates 30 Aug.- 1 Sep. 2000
Title of proceedings Advances in Pattern Recognition : Joint IAPR International Workshops SSPR 2000 and SPR 2000, Alicante, Spain, August 30 – September 1, 2000 proceedings
Editor(s) Ferri, Francesc J.
Inesta, Jose M.
Amin, Adnan
Pudil, Pavel
Publication date 2000
Series Lecture notes in computer science ; 1876
Conference series Joint IAPR International Workshops SSPR and SPR
Start page 236
End page 245
Total pages 10
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) structural pattern recognition
graph matching
graph iso- morphism
database retrieval
database indexing
machine learning
C4.5
Summary Graph matching is an important class of methods in pattern recognition. Typically, a graph representing an unknown pattern is matched with a database of models. If the database of model graphs is large, an additional factor in induced into the overall complexity of the matching process. Various techniques for reducing the influence of this additional factor have been described in the literature. In this paper we propose to extract simple features from a graph and use them to eliminate candidate graphs from the database. The most powerful set of features and a decision tree useful for candidate elimination are found by means of the C4.5 algorithm, which was originally proposed for inductive learning of classication rules. Experimental results are reported demonstrating that effcient candidate elimination can be achieved by the proposed procedure.
Notes 8th International Workshop on Structural and Syntactic Pattern Recognition, 3rd International Workshop on Statistical Techniques in Pattern Recognition
ISBN 9783540679462
3540679464
ISSN 0302-9743
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
Copyright notice ©2000, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30044930

Document type: Conference Paper
Collection: School of Information Technology
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