GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms

Mai F. Tolba, Mohamed Moustafa

2016

Abstract

Boosted cascade of simple features, by Viola Jones, is one of the most famous object detection frameworks. However, it suffers from a lengthy training process. This is due to the vast features space and the exhaustive search nature of Adaboost. In this paper we propose GAdaboost: a Genetic Algorithm to accelerate the training procedure through natural feature selection. Specifically, we propose to limit Adaboost search within a subset of the huge feature space, while evolving this subset following a Genetic Algorithm. Experiments demonstrate that our proposed GAdaboost is up to 3.7 times faster than Adaboost. We also demonstrate that the price of this speedup is a mere decrease (3%, 4%) in detection accuracy when tested on FDDB benchmark face detection set, and Caltech Web Faces respectively.

Paper Citation

in Harvard Style

F. Tolba M. and Moustafa M. (2016). GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms . In Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 156-163. DOI: 10.5220/0006041101560163

in Bibtex Style

@conference{ecta16,
author={Mai F. Tolba and Mohamed Moustafa},
title={GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)},
year={2016},
pages={156-163},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006041101560163},
isbn={978-989-758-201-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 1: ECTA, (IJCCI 2016)
TI - GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms
SN - 978-989-758-201-1
AU - F. Tolba M.
AU - Moustafa M.
PY - 2016
SP - 156
EP - 163
DO - 10.5220/0006041101560163