Probabilistic Relational Indexing

Authors:
Affiliations:
Venue:
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Year:
1998

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Abstract

In this paper we describe a new pattern matching methodology called probabilistic relational indexing that extends the work of Costa and Shapiro [1] [2] to handle uncertainty in pattern recognition. The new technique uses relational models, but avoids the complexity of full graph matching while incorporating probabilistic information that decreases the sensitivity to noise and errors in the data. The probabilistic relational indexing algorithm is compared to two popular decision tree classifiers and with the original discrete relational indexing algorithm.