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The kindest cut: Minimum message length segmentation

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Algorithmic Learning Theory (ALT 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1160))

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Abstract

We consider some particular instances of the segmentation problem. We derive minimum message length (MML) expressions for stating the region boundaries for some one and two dimensional examples. It is found the message length cost of stating region boundaries is dependent on the noise of the data in the separated regions and also the ‘degree of separation’ of the two regions.

The framework given here can be extended to different shaped cuts and also non-constant fits for the regions. Possible applications for the work presented here include its use in tree (i.e. CART) regression and in image segmentation.

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Authors and Affiliations

  1. Department of Computer Science, Monash University, 3168, Clayton, Australia

    Rohan A. Baxter & Jonathan J. Oliver

Authors
  1. Rohan A. Baxter
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  2. Jonathan J. Oliver
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Setsuo Arikawa Arun K. Sharma

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© 1996 Springer-Verlag Berlin Heidelberg

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Baxter, R.A., Oliver, J.J. (1996). The kindest cut: Minimum message length segmentation. In: Arikawa, S., Sharma, A.K. (eds) Algorithmic Learning Theory. ALT 1996. Lecture Notes in Computer Science, vol 1160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61863-5_36

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  • DOI: https://doi.org/10.1007/3-540-61863-5_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61863-8

  • Online ISBN: 978-3-540-70719-6

  • eBook Packages: Springer Book Archive

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