Joint Adaptive Colour Modelling and Skin, Hair and Clothes Segmentation using Coherent Probabilistic Index Maps

We address the joint segmentation of regions around faces into different classes — skin, hair, clothing and background — and the learning of their respective colour models. To this end, we adopt a Bayesian framework with two main elements. First, the modelling and learning of prior beliefs over class colour distributions of different kinds, viz. discrete distributions for background and clothing, and continuous distributions for skin and hair. This component of the model allows the formulation of the colour adaptation task as the computation of the posterior beliefs over class colour distributions given the prior (general) class colour model, a colour likelihood function and observed pixel colours. Second, a spatial prior based on probabilistic index maps enhanced with Markov random field regularisation. The inference of the segmentation and of the adapted colour models is solved using a variational scheme. Segmentation results on a small database of annotated images demonstrate the impact of the different modelling choices.