Universal Seed Skin Segmentation

We present a principled approach for general skin segmentation using graph cuts. We present the idea of a highly adaptive universal seed thereby exploiting the positive training data only. We model the skin segmentation as a min-cut problem on a graph defined by the image color characteristics. The prior graph cuts based approaches for skin segmentation do not provide general skin detection when the information of foreground or background seeds is not available. We propose a concept for processing arbitrary images; using a universal seed to overcome the potential lack of successful seed detections thereby providing basis for general skin segmentation. The advantage of the proposed approach is that it is based on skin sampled training data only making it robust to unseen backgrounds. It exploits the spatial relationship among the neighboring skin pixels providing more accurate and stable skin blobs. Extensive evaluation on a dataset of 8991 images with annotated pixel-level ground truth show that the universal seed approach outperforms other state of the art approaches.