Bayesian Order-Adaptive Clustering for Video Segmentation

  • Peter Orbanz
  • Samuel Braendle
  • Joachim M. Buhmann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4679)


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.


Mixture Model Gibbs Sampler Cluster Solution Dirichlet Process Multivariate Time Series 
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 2007

Authors and Affiliations

  • Peter Orbanz
    • 1
  • Samuel Braendle
    • 1
  • Joachim M. Buhmann
    • 1
  1. 1.Institute of Computational Science, ETH Zurich 

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