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CLC number: TP391.4

On-line Access: 2017-07-31

Received: 2016-07-18

Revision Accepted: 2017-03-14

Crosschecked: 2017-07-11

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jin Zhang

http://orcid.org/0000-0001-7574-2808

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.7 P.1002-1020

http://doi.org/10.1631/FITEE.1601401


Interactive image segmentation with a regression based ensemble learning paradigm


Author(s):  Jin Zhang, Zhao-hui Tang, Wei-hua Gui, Qing Chen, Jin-ping Liu

Affiliation(s):  School of Information Science and Engineering, Central South University, Changsha 410083, China; more

Corresponding email(s):   zhang_jin@csu.edu.cn, zhtang@csu.edu.cn

Key Words:  Interactive image segmentation, Multivariate adaptive regression splines (MARS), Ensemble learning, Thin-plate spline regression (TPSR), Semi-supervised learning, Support vector regression (SVR)


Jin Zhang, Zhao-hui Tang, Wei-hua Gui, Qing Chen, Jin-ping Liu. Interactive image segmentation with a regression based ensemble learning paradigm[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(7): 1002-1020.

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Abstract: 
To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase of manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines (MARS) and smooth thin plate spline regression (TPSR). Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression (SVR) based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the comparison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for interactive natural image segmentation.

基于回归预测集成学习的交互式图像分割

概要:对于复杂场景下的自然图像,全自动图像分割方法难以获得与真实情况吻合的结果,人们常常采用交互式分割手段实现精确分割。然而,当前及背景中存在颜色相似的区域时,传统半监督图像分割方法只能通过大量增加手工标记获得精确分割结果。为此,本文提出一种结合半监督学习的基于回归预测的集成学习交互式图像分割方法。通过集成两个互补的样条回归函数,将图像分割视为一个非线性预测问题。首先,基于已标记样本训练出两个在属性上互补的多元自适应回归样条学习器(multivariate adaptive regression splines, MARS)和薄板样条回归学习器(thin plate spline regression, TPSR);接着,提出一种基于聚类假设和半监督学习的回归器增强算法,该算法从未标记样本中抽选部分样本辅助训练MARS和TPSR;然后,引入支持向量回归方法(support vector regression, SVR)集成MARS和TPSR的预测结果;最后,对SVR集成结果进行GraphCut图像分割。在标准数据库BSDS500和PascalVOC上进行大量实验,验证了所提算法的有效性。大量对比实验证实,所提算法在交互式自然图像分割上的表现与当前最先进算法相当。

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;支持向量回归

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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