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2009
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A new localized superpixel Markov random field for image segmentation

8 years 1 days ago
A new localized superpixel Markov random field for image segmentation
In this paper, we present a novel localized Markov random field (MRF) method based on superpixels for region segmentation. Early vision problems could be formulated as pixel labeling using MRF. But the local interaction in MRF is limited to pixel label comparison. We propose a new localized superpixel Markov random field (SMRF) model to incorporate local data interaction in unsupervised parameter learning. The advantages of the new model include computational efficiency by using superpixel structure and its ability to integrate local knowledge in the learning process. Quantitative evaluation and visual effects show that the new model achieves not only better segmentation accuracy but also lower computational cost than the baseline pixel based model.
Xiaofeng Wang, Xiao-Ping Zhang
Added 19 Feb 2011
Updated 19 Feb 2011
Type Journal
Year 2009
Where ICMCS
Authors Xiaofeng Wang, Xiao-Ping Zhang
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