Multicue graph mincut for image segmentation

  • Authors:

  • Wei Feng;Lei Xie;Zhi-Qiang Liu

  • Affiliations:

  • Media Computing Group, School of Creative Media, City University of Hong Kong, Hong Kong, China;Audio, Speech and Language Processing Group, Shaanxi Provincial Key Lab of Speech & Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi'an, ...;Media Computing Group, School of Creative Media, City University of Hong Kong, Hong Kong, China

  • Venue:
  • ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
  • Year:
  • 2009

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Abstract

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.