Title :
Probabilistic relational indexing
Author :
Chou, Yu-Yu ; Shapiro, Linda G.
Author_Institution :
Washington Univ., Seattle, WA, USA
Abstract :
We describe a new pattern matching methodology called probabilistic relational indexing that extends the work of Costa and Shapiro (1995, 1996) 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
Keywords :
database indexing; pattern matching; probability; relational databases; visual databases; decision tree classifiers; discrete relational indexing algorithm; error sensitivity; full graph matching; noise sensitivity; pattern matching; pattern recognition; probabilistic relational indexing; uncertainty; Application software; Coaxial components; Computer vision; Electrical capacitance tomography; Indexing; Object recognition; Pattern analysis; Pattern recognition; Statistical analysis; Uncertainty;
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-8186-8512-3
DOI :
10.1109/ICPR.1998.711947
