Bayesian Order-Adaptive Clustering for Video Segmentation

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

Abstract

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.

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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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