Learning from Only Positive and Unlabeled Examples for Eyebrow Image Segmentation

Abstract

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.