Learning Graph Laplacian for Image Segmentation

  • Sergey Milyaev
  • Olga Barinova
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7870)


In this paper we formulate the task of semantic image segmentation as a manifold embedding problem and solve it using graph Laplacian approximation. This allows for unsupervised learning of graph Laplacian parameters individually for each image without using any prior information. We perform experiments on GrabCut, Graz and Pascal datasets. At a low computational cost proposed learning method shows comparable performance to choosing the parameters on the test set. Our framework for semantic image segmentation shows better performance than the standard discrete CRF with graph-cut inference.


Image Segmentation Unsupervised Learning Logarithmic Plot Kernel Bandwidth Interactive Segmentation 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sergey Milyaev
    • 1
  • Olga Barinova
    • 2
  1. 1.Radiophysics DepartmentVoronezh State UniversityVoronezhRussia
  2. 2.Department of Computational Mathematics and CyberneticsLomonosov Moscow State UniversityMoscowRussia

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