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Proceedings Paper

An efficient fusion approach for combining human and machine decisions
Author(s): Hyungtae Lee; Heesung Kwon; Ryan M. Robinson; William D. Nothwang; Amar R. Marathe
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Paper Abstract

A novel approach for the fusion of heterogeneous object classification methods is proposed. In order to effectively integrate the outputs of multiple classifiers, the level of ambiguity in each individual classification score is estimated using the precision/recall relationship of the corresponding classifier. The main contribution of the proposed work is a novel fusion method, referred to as Dynamic Belief Fusion (DBF), which dynamically assigns probabilities to hypotheses (target, non-target, intermediate state (target or non-target) based on confidence levels in the classification results conditioned on the prior performance of individual classifiers. In DBF, a joint basic probability assignment, which is obtained from optimally fusing information from all classifiers, is determined by the Dempster's combination rule, and is easily reduced to a single fused classification score. Experiments on RSVP dataset demonstrates that the recognition accuracy of DBF is considerably greater than that of the conventional naive Bayesian fusion as well as individual classifiers used for the fusion.

Paper Details

Date Published: 25 May 2016
PDF: 7 pages
Proc. SPIE 9836, Micro- and Nanotechnology Sensors, Systems, and Applications VIII, 983621 (25 May 2016); doi: 10.1117/12.2220788
Show Author Affiliations
Hyungtae Lee, U.S. Army Research Lab. (United States)
Heesung Kwon, U.S. Army Research Lab. (United States)
Ryan M. Robinson, U.S. Army Research Lab. (United States)
William D. Nothwang, U.S. Army Research Lab. (United States)
Amar R. Marathe, U.S. Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 9836:
Micro- and Nanotechnology Sensors, Systems, and Applications VIII
Thomas George; Achyut K. Dutta; M. Saif Islam, Editor(s)

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