Learning Graph Laplacian for Image Segmentation

  • Sergey Milyaev
  • Olga Barinova
Conference paper

DOI: 10.1007/978-3-642-39759-2_7

Volume 7870 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Milyaev S., Barinova O. (2013) Learning Graph Laplacian for Image Segmentation. In: Gavrilova M.L., Tan C.J.K., Konushin A. (eds) Transactions on Computational Science XIX. Lecture Notes in Computer Science, vol 7870. Springer, Berlin, Heidelberg

Abstract

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.

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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