Improving Kernel Grower Methods using Ellipsoidal Support Vector Data Description

Sabra Hechmi, Alya Slimene, Ezzeddine Zagrouba

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 Harvard Style

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


in Bibtex Style

@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},
}


in EndNote Style

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