Shape retrieval by inexact graph matching

The paper describes a graph matching technique for recognising line-pattern shapes in large image databases. The methodological contribution of the paper is to develop a Bayesian matching algorithm that uses edge consistency and node attribute similarity. This information is used to determine the a posteriori probability of a query graph for each of the candidate matches in the database. The node feature vectors are constructed by computing normalised histograms of pair wise geometric attributes. Attribute similarity is assessed by computing the Bhattacharyya distance between the histograms. Recognition is realised by selecting the candidate from the database which has the largest a posteriori probability. We illustrate the recognition technique on a database containing 2500 line patterns extracted from real world imagery. Here the recognition technique is shown to significantly outperform a number of algorithm alternatives