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Multiple Hypothesis Video Segmentation from Superpixel Flows

  • Amelio Vazquez-Reina
  • Shai Avidan
  • Hanspeter Pfister
  • Eric Miller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

Multiple Hypothesis Video Segmentation (MHVS) is a method for the unsupervised photometric segmentation of video sequences. MHVS segments arbitrarily long video streams by considering only a few frames at a time, and handles the automatic creation, continuation and termination of labels with no user initialization or supervision. The process begins by generating several pre-segmentations per frame and enumerating multiple possible trajectories of pixel regions within a short time window. After assigning each trajectory a score, we let the trajectories compete with each other to segment the sequence. We determine the solution of this segmentation problem as the MAP labeling of a higher-order random field. This framework allows MHVS to achieve spatial and temporal long-range label consistency while operating in an on-line manner. We test MHVS on several videos of natural scenes with arbitrary camera and object motion.

Keywords

Video Sequence Video Stream Processing Window Video Segmentation Label Disagreement 
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 2010

Authors and Affiliations

  • Amelio Vazquez-Reina
    • 1
    • 2
  • Shai Avidan
    • 3
  • Hanspeter Pfister
    • 1
  • Eric Miller
    • 2
  1. 1.School of Engineering and Applied SciencesHarvard UniversityUSA
  2. 2.Department of Computer ScienceTufts UniversityUSA
  3. 3.Adobe Systems Inc.USA

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