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ECCV
2008
Springer

Generative Image Segmentation Using Random Walks with Restart

10 years 4 months ago
Generative Image Segmentation Using Random Walks with Restart
We consider the problem of multi-label, supervised image segmentation when an initial labeling of some pixels is given. In this paper, we propose a new generative image segmentation algorithm for reliable multi-label segmentations in natural images. In contrast to most existing algorithms which focus on the inter-label discrimination, we address the problem of finding the generative model for each label. The primary advantage of our algorithm is that it produces very good segmentation results under two difficult problems: the weak boundary problem and the texture problem. Moreover, single-label image segmentation is possible. These are achieved by designing the generative model with the Random Walks with Restart (RWR). Experimental results with synthetic and natural images demonstrate the relevance and accuracy of our algorithm.
Tae Hoon Kim (Seoul National University), Kyoung M
Added 26 Jul 2009
Updated 02 Apr 2010
Type Conference
Year 2008
Where ECCV
Authors Tae Hoon Kim (Seoul National University), Kyoung Mu Lee (Seoul National University), Sang Uk Lee (Seoul National University)
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