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Online Video Segmentation by Bayesian Split-Merge Clustering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringPohang University of Science and TechnologyPohangKorea
  2. 2.Division of IT Convergence EngineeringPohang University of Science and TechnologyPohangKorea
  3. 3.Department of Creative IT Excellence EngineeringPohang University of Science and TechnologyPohangKorea

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