Ricerca: Codice identificativo = IT/ItRC/00017341
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Tipo: Testo a stampa, Risorsa analitica
É contributo di: Graph-based representations in pattern recognition
Titolo: Bipartite Graph Matching for Computing the Edit Distance of Graphs
Pubblicazione: Berlin ; Heidelberg ; New York : Springer, ©2007
Descrizione fisica: 1-12
Titolo uniforme: Graph-based representations in pattern recognition
Numeri standard: DOI 10.1007/978-3-540-72903-7_1
Sommario o abstract: In the field of structural pattern recognition graphs constitute a very common and powerful way of representing patterns. In contrast to string representations, graphs allow us to describe relational information in the patterns under consideration. One of the main drawbacks of graph representations is that the computation of standard graph similarity measures is exponential in the number of involved nodes. Hence, such computations are feasible for rather small graphs only. One of the most flexible error-tolerant graph similarity measures is based on graph edit distance. In this paper we propose an approach for the efficient compuation of edit distance based on bipartite graph matching by means of MunkresGÇÖ algorithm, sometimes referred to as the Hungarian algorithm. Our proposed algorithm runs in polynomial time, but provides only suboptimal edit distance results. The reason for its suboptimality is that implied edge operations are not considered during the process of finding the optimal node assignment. In experiments on semi-artificial and real data we demonstrate the speedup of our proposed method over a traditional tree search based algorithm for graph edit distance computation. Also we show that classification accuracy remains nearly unaffected
Altre responsabilità: Kaspar Riesen
Michel Neuhaus
Horst Bunke
Classificazione Dewey: 006 ed.22 Metodi speciali di elaborazione
Lingua della pubblicazione: Inglese
Paese di pubblicazione: Germania ; Stati Uniti d'America
Codice identificativo: IT/ItRC/00017341
data di importazione: 01-01-2014
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