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Online Video Segmentation by Bayesian Split-Merge Clustering

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7575)

Abstract

We present an online video segmentation algorithm based on a novel nonparametric Bayesian clustering method called Bayesian Split-Merge Clustering (BSMC). BSMC can efficiently cluster dynamically changing data through split and merge processes at each time step, where the decision for splitting and merging is made by approximate posterior distributions over partitions with Dirichlet Process (DP) priors. Moreover, BSMC sidesteps the difficult problem of finding the proper number of clusters by virtue of the flexibility of nonparametric Bayesian models. We naturally apply BSMC to online video segmentation, which is composed of three steps—pixel clustering, histogram-based merging and temporal matching. We demonstrate the performance of our algorithm on complex real video sequences compared to other existing methods.

Keywords

  • Normalize Mutual Information
  • Dirichlet Process
  • Adjust Rand Index
  • Evolutionary Cluster
  • Initial Partition

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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Lee, J., Kwak, S., Han, B., Choi, S. (2012). Online Video Segmentation by Bayesian Split-Merge Clustering. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7575. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33765-9_61

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  • DOI: https://doi.org/10.1007/978-3-642-33765-9_61

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33764-2

  • Online ISBN: 978-3-642-33765-9

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