Context-based segmentation of image sequences

Jacob Goldberger*, Hayit Greenspan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


We describe an algorithm for context-based segmentation of visual data. New frames in an image sequence (video) are segmented based on the prior segmentation of earlier frames in the sequence. The segmentation is performed by adapting a probabilistic model learned on previous frames, according to the content of the new frame. We utilize the maximum a posteriori version of the EM algorithm to segment the new image. The Gaussian mixture distribution that is used to model the current frame is transformed into a conjugate-prior distribution for the parametric model describing the segmentation of the new frame. This semisupervised method improves the segmentation quality and consistency and enables a propagation of segments along the segmented images. The performance of the proposed approach is illustrated on both simulated and real image data.

Original languageEnglish
Pages (from-to)463-468
Number of pages6
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number3
StatePublished - Mar 2006


  • Conjugate prior
  • Context-based segmentation
  • Image-sequence analysis
  • MAP
  • Model adaptation
  • Video segmentation


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