GeoS: Geodesic Image Segmentation

  • Antonio Criminisi
  • Toby Sharp
  • Andrew Blake
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)


This paper presents GeoS, a new algorithm for the efficient segmentation of n-dimensional image and video data.

The segmentation problem is cast as approximate energy minimization in a conditional random field. A new, parallel filtering operator built upon efficient geodesic distance computation is used to propose a set of spatially smooth, contrast-sensitive segmentation hypotheses. An economical search algorithm finds the solution with minimum energy within a sensible and highly restricted subset of all possible labellings.

Advantages include: i) computational efficiency with high segmentation accuracy; ii) the ability to estimate an approximation to the posterior over segmentations; iii) the ability to handle generally complex energy models. Comparison with max-flow indicates up to 60 times greater computational efficiency as well as greater memory efficiency.

GeoS is validated quantitatively and qualitatively by thorough comparative experiments on existing and novel ground-truth data. Numerous results on interactive and automatic segmentation of photographs, video and volumetric medical image data are presented.


Geodesic Distance Conditional Random Field Global Constraint Video Segmentation Stereo Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Antonio Criminisi
    • 1
  • Toby Sharp
    • 1
  • Andrew Blake
    • 1
  1. 1.Microsoft ResearchCambridgeUK

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