PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Context-based Segmentation of Image Sequences
Jacob Goldberger and Hayit Greenspan
IEEE PAMI Volume 28, Number 3, pp. 463-468, 2006.


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 semi-supervised 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:2677
Deposited By:Jacob Goldberger
Deposited On:22 November 2006