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Large-Scale Integer Programs in Image Analysis

Author

Listed:
  • Geir Dahl

    (University of Oslo, Department of Mathematics and Department of Informatics, P.O. Box 1080, Blindern, 0316 Oslo, Norway)

  • Geir Storvik

    (University of Oslo, Department of Mathematics, P.O. Box 1053, Blindern, 0316 Oslo, Norway)

  • Alice Fadnes

    (University of Oslo, Department of Informatics, P.O. Box 1080, Blindern, 0316 Oslo, Norway)

Abstract

An important problem in image analysis is to segment an image into regions with different class labels. This is relevant in applications in medicine and cartography. In a proper statistical framework this problem may be viewed as a discrete optimization problem. We present two integer linear programming formulations of the problem and study some properties of these models and associated polytopes. Different algorithms for solving these problems are suggested and compared on some realistic data. In particular, a Lagrangian algorithm is shown to have a very promising performance. The algorithm is based on the technique of cost splitting and uses the fact that certain relaxed problems may be solved as shortest path problems.

Suggested Citation

  • Geir Dahl & Geir Storvik & Alice Fadnes, 2002. "Large-Scale Integer Programs in Image Analysis," Operations Research, INFORMS, vol. 50(3), pages 490-500, June.
    • Handle: RePEc:inm:oropre:v:50:y:2002:i:3:p:490-500
    DOI: 10.1287/opre.50.3.490.7741
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    Cited by:

    1. Fred E. Benth & Geir Dahl & Carlo Mannino, 2012. "Computing Optimal Recovery Policies for Financial Markets," Operations Research, INFORMS, vol. 60(6), pages 1373-1388, December.
    2. Fred E. Benth & Geir Dahl & Carlo Mannino, 2010. "Computing optimal recovery policies for financial markets," DIS Technical Reports 2010-20, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".

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