Exact Median Graph Computation Via Graph Embedding
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Median graph: A new exact algorithm using a distance based on the maximum common subgraph
Pattern Recognition Letters
Median graphs: A genetic approach based on new theoretical properties
Pattern Recognition
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
A Recursive Embedding Approach to Median Graph Computation
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
A comparison between two representatives of a set of graphs: median vs. barycenter graph
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Computing maximum association graph in microscopic nucleus images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II
Learning graph prototypes for shape recognition
Computer Vision and Image Understanding
A generic framework for median graph computation based on a recursive embedding approach
Computer Vision and Image Understanding
Network ensemble clustering using latent roles
Advances in Data Analysis and Classification
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
Computer Science Review
On the relation between the common labelling and the median graph
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A comparison between structural and embedding methods for graph classification
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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In this paper, we are interested in the problem of graph clustering. We propose a new algorithm for computing the median of a set of graphs. The concept of median allows the extension of conventional algorithms such as the k-means to graph clustering, helping to bridge the gap between statistical and structural approaches to pattern recognition. Experimental results show the efficiency of the new median graph algorithm compared to the (only) existing algorithm in the literature. We also show its effective use in clustering a set of random graphs and in a content-based synthetic image retrieval system.