GRADUATED ASSIGNMENT ALGORITHM FOR MULTIPLE GRAPH MATCHING BASED ON A COMMON LABELING
In pattern recognition applications, with the aim of increasing efficiency, it is useful to represent the elements by attributed graphs (which consider their structural properties). Under this structural representation of the elements some graph matching problems need a common labeling between the vertices of a set of graphs. Computing this common labeling is a NP-Complete problem. Nevertheless, some methodologies have been presented which obtain a sub-optimal solution in polynomial time. The drawback of these methods is that they rely on pairwise labeling computations, causing the methodologies not to consider the global information during the entire process. To solve this problem, we present a methodology which generates the common labeling by matching all graph nodes to a virtual node set. The method has been tested using three independent datasets, one synthetic and two real. Experimental results show that the presented method obtains better performance than the most popular common labeling algorithm with the same computational cost.
graduated assignment multiple graph matching graph common labeling error tolerant graph isomorphism