基于集成学习的交互式图像分割  

Interactive Image Segmentation Based on Ensemble Learning

作　　者：刘金平[1] 陈青[2] 张进[2] 唐朝晖[2] 

机构地区：[1]湖南师范大学数学与计算机科学学院,湖南长沙410081 [2]中南大学信息科学与工程学院,湖南长沙410083

出　　处：《电子学报》2016年第7期1649-1655,共7页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61472134;No.61171192;No.61272337);湖南师范大学青年基金(No.11405)

摘　　要：针对交互式图像分割人工标记示例匮乏、不同目标区域难以均衡标记,单一分类器难以获得有效分割结果的问题,提出一种多分类器集成学习的交互式图像分割方法.采用多元自适应回归样条(MARS)方法构造第一个分类器;同时引入光滑薄板样条回归函数(TPSR)构造与之互补的第二个分类器,综合组成bagging集成学习器,以降低单一分类器对噪声的敏感度并进一步提高人工标记样本特征空间的利用率.随后,基于半监督学习中的聚类假设,结合bagging多学习器并联特点,提出一种REG-Boosting半监督学习算法,实现半监督图像分割.在不同数据集上的验证性和对比性实验表明所提方法的有效性和优越性.A kind of interactive image segmentation method based on ensemble multi-classifiers is put forward to solve the problem of unsatisfactory segmentation results based on scarce or unbalanced labelling labels on different object ar-eas by single learner.The first classifier is established based on multivariate adaptive regression splines （MARS）method.A complementary thin plate spline regression （TPSR）classifier is simultaneously established.By combination of these two classifiers,a bagging ensemble learner is achieved to reduce the noise sensitivity and make further efforts of improving the use of the feature space information of the labeling samples.Ultimately,a kind of REG-Boosting algorithm for semi-super-vised image segmentation is put forward based on the clustering hypothesis in the ensemble learning combining with the par-allel characteristic of the bagging multi-learners.Abundant validation experiments and comparative experiments on different test sets confirm the effectiveness and out-performance of the proposed method.

关 键 词：交互式图像分割 多元自适应回归样条 集成学习 薄板样条回归 半监督学习 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]