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Published in:
2010 | OriginalPaper | Chapter
Multicue Graph Mincut for Image Segmentation
Authors : Wei Feng, Lei Xie, Zhi-Qiang Liu
Published in: Computer Vision – ACCV 2009
Publisher: Springer Berlin Heidelberg
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We propose a general framework to encode various grouping cues for natural image segmentation. We extend the classical Gibbs energy of an MRF to three terms:
likelihood energy
,
coherence energy
and
separating energy
. We encode
generative cues
in the likelihood and coherence energy to ensure the goodness and feasibility of segmentation, and embed
discriminative cues
in the separating energy to encourage assigning two pixels with strong separability with different labels. We use a self-validated process to iteratively minimize the global Gibbs energy. Our approach is able to automatically determine the number of segments, and produce a natural hierarchy of coarse-to-fine segmentation. Experiments show that our approach works well for various segmentation problems, and outperforms existing methods in terms of robustness to noise and preservation of soft edges.