A fuzzy-interval based approach for explicit graph embedding

Authors:
Muhammad Muzzamil Luqman;Josep Lladós;Jean-Yves Ramel;Thierry Brouard
Affiliations:
Laboratoire d'Informatique, Université François Rabelais de Tours, France and Computer Vision Center, Universitat Autónoma de Barcelona, Spain;Computer Vision Center, Universitat Autónoma de Barcelona, Spain;Laboratoire d'Informatique, Université François Rabelais de Tours, France;Laboratoire d'Informatique, Université François Rabelais de Tours, France
Venue:
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
Year:
2010

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Improving fuzzy multilevel graph embedding through feature selection technique

SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition

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Abstract

We present a new method for explicit graph embedding. Our algorithm extracts a feature vector for an undirected attributed graph. The proposed feature vector encodes details about the number of nodes, number of edges, node degrees, the attributes of nodes and the attributes of edges in the graph. The first two features are for the number of nodes and the number of edges. These are followed by w features for node degrees, m features for k node attributes and n features for l edge attributes -- which represent the distribution of node degrees, node attribute values and edge attribute values, and are obtained by defining (in an unsupervised fashion), fuzzy-intervals over the list of node degrees, node attributes and edge attributes. Experimental results are provided for sample data of ICPR2010 contest GEPR.