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Spatiotemporal Closure

  • Alex Levinshtein
  • Cristian Sminchisescu
  • Sven Dickinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6492)

Abstract

Spatiotemporal segmentation is an essential task for video analysis. The strong interconnection between finding an object’s spatial support and finding its motion characteristics makes the problem particularly challenging. Motivated by closure detection techniques in 2D images, this paper introduces the concept of spatiotemporal closure. Treating the spatiotemporal volume as a single entity, we extract contiguous “tubes” whose overall surface is supported by strong appearance and motion discontinuities. Formulating our closure cost over a graph of spatiotemporal superpixels, we show how it can be globally minimized using the parametric maxflow framework in an efficient manner. The resulting approach automatically recovers coherent spatiotemporal components, corresponding to objects, object parts, and object unions, providing a good set of multiscale spatiotemporal hypotheses for high-level video analysis.

Keywords

Video Sequence Active Contour Graph Construction Motion Segmentation Shot Boundary 
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 2011

Authors and Affiliations

  • Alex Levinshtein
    • 1
  • Cristian Sminchisescu
    • 2
  • Sven Dickinson
    • 1
  1. 1.University of TorontoCanada
  2. 2.University of BonnGermany

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