Skin detection based on multi-seed propagation in a multi-layer graph for regional and color consistency
We propose a new skin detection method based on multi-seeds propagation in a multi-layer graph representation of an image. Initially, some of nodes in the graph are set to be foreground or background seeds based on a simple Bayesian skin detector, and they are propagated through the graph to find the skin probability in the manner of semi-supervised learning. The graph is designed to consider not only local and global coherence but also to consider the color consistency by constructing a multilayer graph of image and cluster layers. Extensive experiments on several datasets are conducted, which demonstrate that our method outperforms the existing methods in terms of various quantitative measures, such as accuracy, precision, recall and F-measure.