正例半监督学习眉毛图像分割

Learning from Only Positive and Unlabeled Examples for Eyebrow Image Segmentation

针对传统交互图像分割方法需要同时标注背景和前景的问题,提出一种新的交互图像分割方法——正例半监督学习图像分割。该方法结合正例半监督学习和图半监督学习,仅需要在感兴趣的图像区域标记少量像素点,就可以完成该区域的分割。在北工大眉毛图像数据库上的实验表明本文提出的方法与图半监督学习、随机游走和Lazy Snapping相比具有更稳定的分割效果。

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-super- vised learning technique, this method only needs users labeling a small number of pixels on interest region for segmentation. Ex- periments on the BJUT Eyebrow Database show that the proposed method achieves analogous results to graph-based semi-super- vised learning, Random Walk as well as Lazy Snapping method, and is suitable for eyebrow recognition preprocessing.