Skin and non-skin probability approximation based on discriminative tree distribution
Sanaa El Fkihi, Mohamed Daoudi, Driss Aboutajdine · 2009
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/non-skin 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.