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Proceedings Paper

Fusion of ATR classifiers
Author(s): Syed A. Rizvi; Dawit Lakew; Nasser M. Nasrabadi
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Paper Abstract

In this paper, we investigate several fusion techniques for designing a composite classifier to improve the performance (probability of correct classification) of FLIR ATR. The motivation behind the fusion of ATR algorithms is that if each contributing technique in a fusion algorithm (composite classifier) emphasizes on learning at least some features of the targets that are not learned by other contributing techniques for making a classification decision, a fusion of ATR algorithms may improve overall probability of correct classification of the composite classifier. In this research, we propose to use four ATR algorithms for fusion. We propose to use averaged Bayes classifier, committee of experts, stacked-generalization, winner-takes-all, and ranking-based fusion techniques for designing the composite classifiers. The experimental results show an improvement of more than 5 % over the best individual performance.

Paper Details

Date Published: 5 April 2002
PDF: 12 pages
Proc. SPIE 4668, Applications of Artificial Neural Networks in Image Processing VII, (5 April 2002); doi: 10.1117/12.461670
Show Author Affiliations
Syed A. Rizvi, CUNY/College of Staten Island (United States)
Dawit Lakew, CUNY/College of Staten Island (United States)
Nasser M. Nasrabadi, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4668:
Applications of Artificial Neural Networks in Image Processing VII
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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