计算机与现代化Issue(9)：127-133,7.DOI:10.3969/j.issn.1006-2475.2012.09.031
正例半监督学习眉毛图像分割
Learning from Only Positive and Unlabeled Examples for Eyebrow Image Segmentation
摘要
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.关键词
正例半监督学习/图半监督学习/交互图像分割/朴素贝叶斯/期望最大化Key words
learning from only positive and unlabeled examples/graph-based semi-supervised learning/interactively image segmentation/naive Bayes/expectation-maximization分类
信息技术与安全科学引用本文复制引用
张夏欢,李玉鑑,张晨光..正例半监督学习眉毛图像分割[J].计算机与现代化,2012,(9):127-133,7.基金项目
国家自然科学基金资助项目(61175004,60775010) （61175004,60775010）
北京市自然科学基金资助项目(4112009,4113067,4113068) （4112009,4113067,4113068）
北京市教委科技发展项目(KZ201210005007,KM201010005012) （KZ201210005007,KM201010005012）
北京工业大学高层次人才培养项目 （）
