Authors:
Sanaa El Fkihi
1
;
Mohamed Daoudi
2
and
Driss Aboutajdine
3
Affiliations:
1
GET/TELECOM Lille1, LIFL (UMR USTL-CNRS 8022); GSCM LRIT Faculty of Sciences, University Mohammed V, Morocco
;
2
GET/TELECOM Lille1, LIFL (UMR USTL-CNRS 8022), France
;
3
GSCM LRIT Faculty of Sciences, University Mohammed V, Morocco
Keyword(s):
Optimal spanning tree, dependency tree, mixture of trees, skin detection, classification, probability graphical models, tree distributions.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Image and Video Analysis
;
Statistical Approach
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.