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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. Attached Files Name Description MIMEType Size Downloads 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 Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 01 May 2012, 11:07:11 EST Tue, 01 May 2012, 12:14:38 EST Tue, 01 May 2012, 12:17:37 EST Filtered Full Access Statistics: 74 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Tue, 01 May 2012, 11:07:11 EST

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