Sub-Markov Random Walk for Image Segmentation

Dong, Xingping, Shen, Jianbing, Shao, Ling and Van Gool, Luc (2016) Sub-Markov Random Walk for Image Segmentation. IEEE Transactions on Image Processing, 25 (2). pp. 516-527. ISSN 1057-7149

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Official URL: http://dx.doi.org/10.1109/TIP.2015.2505184

Abstract

A novel sub-Markov random walk (subRW) algorithm with label prior is proposed for seeded image segmentation, which can be interpreted as a traditional random walker on a graph with added auxiliary nodes. Under this explanation, we unify the proposed subRW and other popular random walk (RW) algorithms. This unifying view will make it possible for transferring intrinsic findings between different RW algorithms, and offer new ideas for designing novel RW algorithms by adding or changing auxiliary nodes. To verify the second benefit, we design a new subRW algorithm with label prior to solve the segmentation problem of objects with thin and elongated parts. The experimental results on both synthetic and natural images with twigs demonstrate that the proposed subRW method outperforms previous RW algorithms for seeded image segmentation.

Item Type: Article
Uncontrolled Keywords: complex texture, Seeded image segmentation, subMarkov, random walk, optimization, label prior
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 06 Feb 2017 16:23
Last Modified: 06 Feb 2017 16:23
URI: http://nrl.northumbria.ac.uk/id/eprint/29522

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