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Semi-supervised Segmentation Based on Non-local Continuous Min-Cut

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Scale Space and Variational Methods in Computer Vision (SSVM 2009)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5567))

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Abstract

We propose a semi-supervised image segmentation method that relies on a non-local continuous version of the min-cut algorithm and labels or seeds provided by a user. The segmentation process is performed via energy minimization. The proposed energy is composed of three terms. The first term defines labels or seed points assigned to objects that the user wants to identify and the background. The second term carries out the diffusion of object and background labels and stops the diffusion when the interface between the object and the background is reached. The diffusion process is performed on a graph defined from image intensity patches. The graph of intensity patches is known to better deal with textures because this graph uses semi-local and non-local image information. The last term is the standard TV term that regularizes the geometry of the interface. We introduce an iterative scheme that provides a unique minimizer. Promising results are presented on synthetic textures a nd real-world images.

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Author information

Authors and Affiliations

  1. Signal Processing Laboratory 5, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland

    Nawal Houhou & Jean-Philippe Thiran

  2. Department of Mathematics, University of California, Los Angeles, CA 90095-1555, USA

    Xavier Bresson, Arthur Szlam & Tony F. Chan

Authors
  1. Nawal Houhou
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  2. Xavier Bresson
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  3. Arthur Szlam
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  4. Tony F. Chan
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  5. Jean-Philippe Thiran
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Editor information

Editors and Affiliations

  1. Department of Mathematics, University of Bergen, Johannes Brunsgate 12, 5008, Bergen, Norway

    Xue-Cheng Tai

  2. Department of Informatics and Centre of Mathematics for Applications, University of Oslo, Oslo, Norway

    Knut Mørken

  3. Simula Research Laboratory, M. Linges v 17, Fornebu, P.O.Box 134, N-1325, Lysaker, Norway

    Marius Lysaker

  4. SINTEF ICT, Oslo, Norway

    Knut-Andreas Lie

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© 2009 Springer-Verlag Berlin Heidelberg

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Houhou, N., Bresson, X., Szlam, A., Chan, T.F., Thiran, JP. (2009). Semi-supervised Segmentation Based on Non-local Continuous Min-Cut. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_10

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  • DOI: https://doi.org/10.1007/978-3-642-02256-2_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02255-5

  • Online ISBN: 978-3-642-02256-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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