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Authors: Mai F. Tolba and Mohamed Moustafa

Affiliation: The American University in Cairo, Egypt

ISBN: 978-989-758-201-1

Keyword(s): Object Detection, Genetic Algorithms, Haar Features, Adaboost, Face Detection.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

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.

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Paper citation in several formats:
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 3: ECTA, (IJCCI 2016) ISBN 978-989-758-201-1, pages 156-163. DOI: 10.5220/0006041101560163

@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 3: ECTA, (IJCCI 2016)},
year={2016},
pages={156-163},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006041101560163},
isbn={978-989-758-201-1},
}

TY - CONF

JO - Proceedings of the 8th International Joint Conference on Computational Intelligence - Volume 3: 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

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