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EMMCVPR
2007
Springer

Bayesian Order-Adaptive Clustering for Video Segmentation

14 years 4 months ago
Bayesian Order-Adaptive Clustering for Video Segmentation
Video segmentation requires the partitioning of a series of images into groups that are both spatially coherent and smooth along the time axis. We formulate segmentation as a Bayesian clustering problem. Context information is propagated over time by a conjugate structure. The level of segment resolution is controlled by a Dirichlet process prior. Our contributions include a conjugate nonparametric Bayesian model for clustering in multivariate time series, a MCMC inference algorithm, and a multiscale sampling approach for Dirichlet process mixture models. The multiscale algorithm is applicable to data with a spatial structure. The method is tested on synthetic data and on videos from the MPEG4 benchmark set.
Peter Orbanz, Samuel Braendle, Joachim M. Buhmann
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where EMMCVPR
Authors Peter Orbanz, Samuel Braendle, Joachim M. Buhmann
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