Share Email Print

Proceedings Paper

Quantifying the performance of fused correlated multiple classifiers
Author(s): Christine M. Schubert; Mark E. Oxley; Kenneth W. Bauer
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The Receiver Operating Characteristic (ROC) curve is typically used to quantify the performance of Automatic Target Recognition (ATR) systems. When multiple classifiers are to be fused, assumptions must be made in order to mathematically combine the individual ROC curves for each of these classifiers in order to form one fused ROC curve. Often, one of these assumptions is independence between the classifiers. However, correlation may exist between the classifiers, processors, sensors and the outcomes used to generate each ROC curve. This paper will demonstrate a method for creating a ROC curve of the fused classifiers which incorporates the correlation that exists between the individual ROC curves. Specifically, we will use the derived covariance matrix between multiple classifiers to compute the existing correlation and level of dependence between pairs of classifiers. The ROC curve for the fused system is then produced, adjusting for this level of dependency, using a given fusion rule.

Paper Details

Date Published: 25 May 2005
PDF: 12 pages
Proc. SPIE 5809, Signal Processing, Sensor Fusion, and Target Recognition XIV, (25 May 2005); doi: 10.1117/12.602956
Show Author Affiliations
Christine M. Schubert, Air Force Institute of Technology (United States)
Mark E. Oxley, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)

Published in SPIE Proceedings Vol. 5809:
Signal Processing, Sensor Fusion, and Target Recognition XIV
Ivan Kadar, Editor(s)

© SPIE. Terms of Use
Back to Top