25 May 2016 An efficient fusion approach for combining human and machine decisions
Author Affiliations +
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.
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hyungtae Lee, Heesung Kwon, Ryan M. Robinson, William D. Nothwang, Amar R. Marathe, "An efficient fusion approach for combining human and machine decisions", Proc. SPIE 9836, Micro- and Nanotechnology Sensors, Systems, and Applications VIII, 983621 (25 May 2016); doi: 10.1117/12.2220788; https://doi.org/10.1117/12.2220788
PROCEEDINGS
7 PAGES


SHARE
RELATED CONTENT

Subpixel Target Detection And Tracking
Proceedings of SPIE (March 27 1987)
Use of boosting to improve LVQ ATR classifiers
Proceedings of SPIE (April 05 2002)
Performance characterization of edge detectors
Proceedings of SPIE (March 01 1992)
Imaging Laser Radar Recognition Processing
Proceedings of SPIE (March 01 1990)

Back to Top