Embedding Gestalt Laws on Conditional Random Field for Image Segmentation

  • Olfa Besbes
  • Nozha Boujemaa
  • Ziad Belhadj
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)


We propose a higher order conditional random field built over a graph of superpixels for partitioning natural images into coherent segments. Our model operates at both superpixel and segment levels and includes potentials that capture similarity, proximity, curvilinear continuity and familiar configuration. For a given image, these potentials enforce consistency and regularity of labellings. The optimal one should maximally satisfy local, pairwise and global constraints imposed respectively by the learned association, interaction and higher order potentials. Experiments on a variety of natural images show that integration of higher order potentials qualitatively and quantitatively improves results and leads to more coherent and regular segments.


Image Segmentation Pattern Anal Natural Image High Order Potential Proposal Probability 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Olfa Besbes
    • 1
  • Nozha Boujemaa
    • 2
  • Ziad Belhadj
    • 1
  1. 1.URISA - SUPCOM, Parc TechnologiqueArianaTunisia
  2. 2.INRIA Saclay Île-de-FranceOrsayFrance

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