OPTIMAL SPANNING TREES MIXTURE BASED PROBABILITY APPROXIMATION FOR SKIN DETECTION

Sanaa El Fkihi, Mohamed Daoudi, Driss Aboutajdine

2007

Abstract

In this paper we develop a new skin detection algorithm for learning in color images. Our contribution is based on the Optimal Spanning Tree distributions that are widely used in many optimization areas. Thus, by making some assumptions we propose the mixture of the Optimal Spanning Trees to approximate the true Skin (or Non-Skin) class probability in a supervised algorithm. The theoretical proof of the Optimal Spanning Trees’ mixture is drawn. Furthermore, the performance of our method is assessed on the Compaq database by measuring the Receiver Operating Characteristic curve and its under area. These measures have proved better results of the proposed model compared with the results of a random Optimal Spanning Tree model and the baseline one.

Paper Citation

in Harvard Style

El Fkihi S., Daoudi M. and Aboutajdine D. (2007). OPTIMAL SPANNING TREES MIXTURE BASED PROBABILITY APPROXIMATION FOR SKIN DETECTION . In Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 978-972-8865-73-3, pages 382-385. DOI: 10.5220/0002046303820385

in Bibtex Style

@conference{visapp07,
author={Sanaa El Fkihi and Mohamed Daoudi and Driss Aboutajdine},
title={OPTIMAL SPANNING TREES MIXTURE BASED PROBABILITY APPROXIMATION FOR SKIN DETECTION},
booktitle={Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2007},
pages={382-385},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002046303820385},
isbn={978-972-8865-73-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the Second International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - OPTIMAL SPANNING TREES MIXTURE BASED PROBABILITY APPROXIMATION FOR SKIN DETECTION
SN - 978-972-8865-73-3
AU - El Fkihi S.
AU - Daoudi M.
AU - Aboutajdine D.
PY - 2007
SP - 382
EP - 385
DO - 10.5220/0002046303820385