Proceedings chapter
Open access

Nonlinear Temporal Modeling for Motion-based Video Overviewing

Presented at Rennes (France), Sept. 22-24
Publication date2003

This paper presents a method for video collection overviewing based the dynamic content of the scenes. In an unsupervised context, our approach relies on the nonlinear temporal modeling of wavelet-based motion features directly estimated from the image sequence. Based on SVM-regression, the nonlinear model is able to learn the behavior of the motion descriptors along the temporal dimension and to catch useful informations of the dynamic content. A similarity measure associated to the temporal model is then defined. It allows to compare video segments according to motion descriptors and thus defines a high-dimensional feature space where the video sequences under investigation are projected. The Curvilinear Component Analysis algorithm is finally used to map the feature space onto a 2D space. This operation enables us to display the video collection and gives an overview of the content according to motion features.

  • Nonlinear temporal modeling
  • Motion-based video overviewing
  • Dynamic content
  • Motion descriptor
  • Video collection
  • High-dimensional feature space
  • Motion feature
  • Image sequence
  • Temporal model
  • 2d space feature space
  • Video sequence
  • Video segment
  • Approach relies
  • Similarity measure
  • Curvilinear component analysis algorithm
  • wavelet-based motion feature
  • Unsupervised context
  • Temporal dimension
  • Nonlinear model
  • Useful information
Citation (ISO format)
BRUNO, Eric, MARCHAND-MAILLET, Stéphane. Nonlinear Temporal Modeling for Motion-based Video Overviewing. In: Proceedings of the 3rd International Workshop on Content-Based Multimedia Indexing, CBMI′03. Rennes (France). [s.l.] : [s.n.], 2003.
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Proceedings chapter (Published version)
  • PID : unige:47726

Technical informations

Creation03/06/2015 5:12:10 PM
First validation03/06/2015 5:12:10 PM
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