GeoS: Geodesic Image Segmentation

  • Authors:
  • Antonio Criminisi;Toby Sharp;Andrew Blake

  • Affiliations:
  • Microsoft Research, Cambridge, UK;Microsoft Research, Cambridge, UK;Microsoft Research, Cambridge, UK

  • Venue:
  • ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
  • Year:
  • 2008

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Abstract

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 andautomatic segmentation of photographs, video and volumetric medical image data are presented.