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Supervoxel-Consistent Foreground Propagation in Video

  • Suyog Dutt Jain
  • Kristen Grauman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8692)

Abstract

A major challenge in video segmentation is that the foreground object may move quickly in the scene at the same time its appearance and shape evolves over time. While pairwise potentials used in graph-based algorithms help smooth labels between neighboring (super)pixels in space and time, they offer only a myopic view of consistency and can be misled by inter-frame optical flow errors. We propose a higher order supervoxel label consistency potential for semi-supervised foreground segmentation. Given an initial frame with manual annotation for the foreground object, our approach propagates the foreground region through time, leveraging bottom-up supervoxels to guide its estimates towards long-range coherent regions. We validate our approach on three challenging datasets and achieve state-of-the-art results.

Keywords

Markov Random Field Foreground Object Object Segmentation Foreground Region Video Segmentation 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Suyog Dutt Jain
    • 1
  • Kristen Grauman
    • 1
  1. 1.University of Texas at AustinUSA

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