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Authors: Sabra Hechmi ; Alya Slimene and Ezzeddine Zagrouba

Affiliation: Tunis El Manar University, Tunisia

ISBN: 978-989-758-018-5

Keyword(s): Kernel-based methods, Ellipsoidal Support Vector Data Description, Kernel Grower.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Geometry and Modeling ; Image-Based Modeling ; Kernel Methods ; Pattern Recognition ; Software Engineering ; Theory and Methods

Abstract: In these recent years, kernel methods have gained a considerable interest in many areas of machine learning. This work investigates the ability of kernel clustering methods to deal with one of the meaningful problem of computer vision namely image segmentation task. In this context, we propose a novel kernel method based on an Ellipsoidal Support Vector Data Description ESVDD. Experiments conducted on a selected synthetic data sets and on Berkeley image segmentation benchmark show that our approach significantly outperforms state-of-the-art kernel methods.

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Paper citation in several formats:
Hechmi, S.; Slimene, A. and Zagrouba, E. (2014). Improving Kernel Grower Methods using Ellipsoidal Support Vector Data Description.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 343-349. DOI: 10.5220/0004923203430349

@conference{icpram14,
author={Sabra Hechmi. and Alya Slimene. and Ezzeddine Zagrouba.},
title={Improving Kernel Grower Methods using Ellipsoidal Support Vector Data Description},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={343-349},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004923203430349},
isbn={978-989-758-018-5},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Improving Kernel Grower Methods using Ellipsoidal Support Vector Data Description
SN - 978-989-758-018-5
AU - Hechmi, S.
AU - Slimene, A.
AU - Zagrouba, E.
PY - 2014
SP - 343
EP - 349
DO - 10.5220/0004923203430349

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