This paper describes a graph-matching technique for recognising line-patter
n shapes in large image databases. The methodological contribution of the p
aper 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 norm
alised histograms of pairwise geometric attributes. Attribute similarity is
assessed by computing the Bhattacharyya distance between the histograms. R
ecognition is realised by selecting the candidate from the database which h
as the largest a posteriori probability. (C) 1999 Elsevier Science B.V. All
rights reserved.