Abstract
Graph centrality has been extensively applied in Social Network Analysis to model the interaction of actors and the information flow inside a graph. In this paper, we investigate the usage of graph centralities in the Shape Matching task. We create a graph-based representation of a shape and describe this graph by using different centrality measures. We build a Naive Bayes classifier whose input feature vector consists of the measurements obtained by the centralities and evaluate the different performances for each centrality.
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de Sousa, S., Artner, N.M., Kropatsch, W.G. (2013). On the Evaluation of Graph Centrality for Shape Matching. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2013. Lecture Notes in Computer Science, vol 7877. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38221-5_22
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DOI: https://doi.org/10.1007/978-3-642-38221-5_22
Publisher Name: Springer, Berlin, Heidelberg
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