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Authors: Jeonghwan Park ; Kang Li and Huiyu Zhou

Affiliation: Queen's University Belfast, United Kingdom

Keyword(s): Feature Selection, Appearance Model, Human Detection.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image Understanding ; Image-Based Modeling ; Object Recognition ; Pattern Recognition ; Shape Representation ; Software Engineering

Abstract: We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid feature selection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimal feature vector that well represents the shapes of the subjects in the images. In detail, the proposed feature selection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while the standard linear support vector machine (SVM) is used as the classifier for human detection. We apply the proposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCAL VOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approach can improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy. Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach with around 9% improvement in the detection accuracy. (More)

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Paper citation in several formats:
Park, J.; Li, K. and Zhou, H. (2016). k-fold Subsampling based Sequential Backward Feature Elimination. In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-173-1; ISSN 2184-4313, SciTePress, pages 423-430. DOI: 10.5220/0005688804230430

@conference{icpram16,
author={Jeonghwan Park. and Kang Li. and Huiyu Zhou.},
title={k-fold Subsampling based Sequential Backward Feature Elimination},
booktitle={Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2016},
pages={423-430},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005688804230430},
isbn={978-989-758-173-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - k-fold Subsampling based Sequential Backward Feature Elimination
SN - 978-989-758-173-1
IS - 2184-4313
AU - Park, J.
AU - Li, K.
AU - Zhou, H.
PY - 2016
SP - 423
EP - 430
DO - 10.5220/0005688804230430
PB - SciTePress

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