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International Conference on Scale-Space Theories in Computer Vision

Scale-Space 2003: Scale Space Methods in Computer Vision pp 266–280Cite as

Mode Estimation Using Pessimistic Scale Space Tracking

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2695))

Abstract

Estimation of the mode of a distribution over ℝn from discrete samples is introduced and three methods for its solution are developed and evaluated. The first solution is based on Fréchet’s definition of central tendencies. We show that algorithms based on this approach have only limited success due to the non-differentiability of the Fréchet measures. The second solution is based on tracking maxima through a Scale Space built from the samples. We show that this is more accurate than the Fréchet approach, but that tracking to very fine scales is unwarranted and undesirable. For our third method we analyze the reliability of the information across scale using an exact bootstrap analysis. This leads to a modified version of the Scale Space approach where unreliable information is downgraded (pessimistically) so that tracking into such regions does not occur. This modification improves the accuracy of mode estimation. We conclude with demonstrations on high-dimensional real and synthetic data, which confirm the technique’s accuracy and utility.

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Author information

Authors and Affiliations

  1. Radiological Sciences, 5th Floor Thomas Guy House Guys Campus, London, SE1 9RT, UK

    Lewis D. Griffin

  2. IT University of Copenhagen, Glentevej 67-69, DK 2400, Copenhagen, Denmark

    Martin Lillholm

Authors
  1. Lewis D. Griffin
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  2. Martin Lillholm
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Editor information

Editors and Affiliations

  1. Imaging Sciences, 5th Floor Thomas Guy House, Guy’s Campus, London, SE1 9RT, UK

    Lewis D. Griffin

  2. IT University of Copenhagen, Glentevej 67-69, Copenhagen NV, Denmark

    Martin Lillholm

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© 2003 Springer-Verlag Berlin Heidelberg

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Griffin, L.D., Lillholm, M. (2003). Mode Estimation Using Pessimistic Scale Space Tracking. In: Griffin, L.D., Lillholm, M. (eds) Scale Space Methods in Computer Vision. Scale-Space 2003. Lecture Notes in Computer Science, vol 2695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44935-3_19

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  • DOI: https://doi.org/10.1007/3-540-44935-3_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40368-5

  • Online ISBN: 978-3-540-44935-5

  • eBook Packages: Springer Book Archive

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