Interactive image segmentation based on ensemble learning

被引：0

作者：

Liu J.-P. ^{[1
]}

Chen Q. ^{[2
]}

Zhang J. ^{[2
]}

Tang Z.-H. ^{[2
]}

机构：

[1] College of Mathematics and Computer Science, Hunan Normal University, Changsha, 410081, Hunan

[2] School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2016年 / 44卷 / 07期

关键词：

Ensemble learning; Interactive image segmentation; Multivariate adaptive regression splines (MARS); Semi-supervised learning; Thin-plate spline regression(TPSR);

D O I：

10.3969/j.issn.0372-2112.2016.07.019

中图分类号：

学科分类号：

摘要：

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 areas 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-supervised image segmentation is put forward based on the clustering hypothesis in the ensemble learning combining with the parallel 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. © 2016, Chinese Institute of Electronics. All right reserved.

引用

页码：1649 / 1655

页数：6

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