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Abstract:
This paper describes a Bayesian graph matching algorithm for
data-mining from large structural data-bases. The matching
algorithm uses edge-consistency and node attribute similarity to
determine the a posteriori probability of a query graph for each of
the candidate matches in the data-base. The node feature-vectors
are constructed by computing normalised histograms of pairwise
geometric attributes. Attribute similarity is assessed by computing
the Bhattacharyya distance between the histograms. Recognition is
realised by selecting the candidate from the data-base which has
the largest a posteriori probability. We illustrate the recognition
technique on a data-base containing 2500 line patterns extracted
from real-world imagery. Here the recognition technique is shown to
significantly outperform a number of algorithm alternatives.
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