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Global Energy Minimization: A Transformation Approach

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Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2134))

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Abstract

This paper addresses the problem of minimizing an energy function by means of a monotonic transformation. With an observation on global optimality of functions under such a transformation, we show that a simple and effective algorithm can be derived to search within possible regions containing the global optima. Numerical experiments are performed to compare this algorithm with one that does not incorporate transformed information using several benchmark problems. These results are also compared to best known global search algorithms in the literature. In addition, the algorithm is shown to be useful for a class of neural network learning problems, which possess much larger parameter spaces.

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Author information

Authors and Affiliations

  1. Centre for Signal Processing School of Electrical & Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798

    Kar-Ann Toh

Authors
  1. Kar-Ann Toh
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Editor information

Editors and Affiliations

  1. Instituto de Telecomunicacoes and Instituto Superior Tecnico, 1049-001, Lisboa, Portugal

    Mário Figueiredo

  2. INRIA, 2004 Route des Lucioles, 06902 Sophia-Antipolis cedex, France

    Josiane Zerubia

  3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, 48824, USA

    Anil K. Jain

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© 2001 Springer-Verlag Berlin Heidelberg

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Toh, KA. (2001). Global Energy Minimization: A Transformation Approach. In: Figueiredo, M., Zerubia, J., Jain, A.K. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2001. Lecture Notes in Computer Science, vol 2134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44745-8_26

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  • DOI: https://doi.org/10.1007/3-540-44745-8_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42523-6

  • Online ISBN: 978-3-540-44745-0

  • eBook Packages: Springer Book Archive

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