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A First Derivative Potts Model for Segmentation and Denoising Using ILP

In: Operations Research Proceedings 2017

Author

Listed:
  • Ruobing Shen

    (Heidelberg University)

  • Gerhard Reinelt

    (Heidelberg University)

  • Stephane Canu

    (Normandie University, INSA Rouen)

Abstract

Unsupervised image segmentation and denoising are two fundamental tasks in image processing. Usually, graph based models such as multicut are used for segmentation and variational models are employed for denoising. Our approach addresses both problems at the same time. We propose a novel ILP formulation of the first derivative Potts model with the $$\ell _1$$ data term, where binary variables are introduced to deal with the $$\ell _0$$ norm of the regularization term. The ILP is then solved by a standard off-the-shelf MIP solver. Numerical experiments are compared with the multicut problem.

Suggested Citation

  • Ruobing Shen & Gerhard Reinelt & Stephane Canu, 2018. "A First Derivative Potts Model for Segmentation and Denoising Using ILP," Operations Research Proceedings, in: Natalia Kliewer & Jan Fabian Ehmke & Ralf Borndörfer (ed.), Operations Research Proceedings 2017, pages 53-59, Springer.
    • Handle: RePEc:spr:oprchp:978-3-319-89920-6_8
    DOI: 10.1007/978-3-319-89920-6_8
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    Cited by:

    1. Ruobing Shen & Bo Tang & Leo Liberti & Claudia D’Ambrosio & Stéphane Canu, 2021. "Learning discontinuous piecewise affine fitting functions using mixed integer programming over lattice," Journal of Global Optimization, Springer, vol. 81(1), pages 85-108, September.

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