Weighted minimum common supergraph for cluster representation

Graphs are a powerful and versatile tool useful for representing patterns in various fields of science and engineering. In many applications, for example, in image processing and pattern recognition, it is required to measure the similarity of objects for clustering similar patterns. In this paper a new structural method for representing a cluster of graphs is proposed. Using this method it becomes easy to extract the common information shared in the patterns of a cluster, make evident this information and separate it from noise and distortions that usually affect graph representation of real images.