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International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition

EMMCVPR 1997: Energy Minimization Methods in Computer Vision and Pattern Recognition pp 229–244Cite as

Self annealing: Unifying deterministic annealing and relaxation labeling

  • Deterministic Methods
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1223))

Abstract

Deterministic annealing and relaxation labeling algorithms for classification and matching are presented and discussed. A new approach —self annealing—is introduced to bring deterministic annealing and relaxation labeling into accord. Self annealing results in an emergent linear schedule for winner-take-all and assignment problems. Also, the relaxation labeling algorithm can be seen as an approximation to the self annealing algorithm for matching and labeling problems.

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Author information

Authors and Affiliations

  1. Department of Diagnostic Radiology, Yale University, New Haven, CT, USA

    Anand Rangarajan

Authors
  1. Anand Rangarajan
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Editor information

Marcello Pelillo Edwin R. Hancock

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© 1997 Springer-Verlag Berlin Heidelberg

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Rangarajan, A. (1997). Self annealing: Unifying deterministic annealing and relaxation labeling. In: Pelillo, M., Hancock, E.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 1997. Lecture Notes in Computer Science, vol 1223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62909-2_83

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  • DOI: https://doi.org/10.1007/3-540-62909-2_83

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62909-2

  • Online ISBN: 978-3-540-69042-9

  • eBook Packages: Springer Book Archive

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