Learning from Only Positive and Unlabeled Examples for Eyebrow Image Segmentation
Zhang Xia-huan, Yujian Li, Chenguang Zhang · Computer and Modernization · 2012
Traditional interactive image segmentation methods require users giving out background as well as foreground scribbles.Aiming at this problem,this paper proposes a novel image segmentation framework,named image segmentation with only positive and unlabeled examples.By combining learning from only positive and unlabeled examples method with graph-based semi-supervised learning technique,this method only needs users labeling a small number of pixels on interest region for segmentation.Experiments on the BJUT Eyebrow Database show that the proposed method achieves analogous results to graph-based semi-supervised learning,Random Walk as well as Lazy Snapping method,and is suitable for eyebrow recognition preprocessing.