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Authors: Miroslav Kobetski and Josephine Sullivan

Affiliation: KTH, Sweden

Keyword(s): Boosting, Image Classification, Algorithm Evaluation, Dataset Pruning, VOC2007.

Related Ontology Subjects/Areas/Topics: Applications ; Classification ; Ensemble Methods ; Object Recognition ; Pattern Recognition ; Software Engineering ; Theory and Methods

Abstract: In visual object class recognition it is difficult to densely sample the set of positive examples. Therefore, frequently there will be areas of the feature space that are sparsely populated, in which uncommon examples are hard to disambiguate from surrounding negatives without overfitting. Boosting in particular struggles to learn optimal decision boundaries in the presence of such hard and ambiguous examples. We propose a twopass dataset pruning method for identifying ambiguous examples and subjecting them to an exclusion function, in order to obtain more optimal decision boundaries for existing boosting algorithms. We also provide an experimental comparison of different boosting algorithms on the VOC2007 dataset, training them with and without our proposed extension. Using our exclusion extension improves the performance of all the tested boosting algorithms except TangentBoost, without adding any additional test-time cost. In our experiments LogitBoost performs best overall and is also significantly improved by our extension. Our results also suggest that outlier exclusion is complementary to positive jittering and hard negative mining. (More)

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Paper citation in several formats:
Kobetski, M. and Sullivan, J. (2013). Improved Boosting Performance by Exclusion of Ambiguous Positive Examples. In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-8565-41-9; ISSN 2184-4313, SciTePress, pages 11-21. DOI: 10.5220/0004204400110021

@conference{icpram13,
author={Miroslav Kobetski. and Josephine Sullivan.},
title={Improved Boosting Performance by Exclusion of Ambiguous Positive Examples},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2013},
pages={11-21},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004204400110021},
isbn={978-989-8565-41-9},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Improved Boosting Performance by Exclusion of Ambiguous Positive Examples
SN - 978-989-8565-41-9
IS - 2184-4313
AU - Kobetski, M.
AU - Sullivan, J.
PY - 2013
SP - 11
EP - 21
DO - 10.5220/0004204400110021
PB - SciTePress

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