Skin and non-skin probability approximation based on discriminative tree distribution

Authors:
S. El Fkihi;M. Daoudi;D. Aboutajdine
Affiliations:
LRIT unité associée au CNRST, Faculty of Sciences, MohammedV University-Agdal, Rabat, Morocco;Institut TELECOM, TELECOM Lille1, LIFL, UMR, Villeneuve d'Ascq, France;LRIT unité associée au CNRST, Faculty of Sciences, MohammedV University-Agdal, Rabat, Morocco
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

We investigate the probability tree models to approximate skin and non-skin distributions. These models have presented good results in solving the skin detection problem. However, there are two main disadvantages of the existing skin/nonskin tree distributions based models: (1) the structure of some tree distributions is predefined; and (2) the inter and the intra classes of skin/non-skin are not taken into account at the same time by the existing skin and/or non-skin tree models. To overcome these drawbacks, we propose a new classifier based on an image patch joint distribution approximation modelled by a discriminative skin/non-skin tree. On the Compaq database, we examine the performances of the proposed approach compared with the baseline model and two others based on dependency tree's distributions. Experimental results show that the new approach is a significant improvement over the others.