How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye Movements

  • Wolf Kienzle
  • Bernhard Schölkopf
  • Felix A. Wichmann
  • Matthias O. Franz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)

Abstract

Interest point detection in still images is a well-studied topic in computer vision. In the spatiotemporal domain, however, it is still unclear which features indicate useful interest points. In this paper we approach the problem by learning a detector from examples: we record eye movements of human subjects watching video sequences and train a neural network to predict which locations are likely to become eye movement targets. We show that our detector outperforms current spatiotemporal interest point architectures on a standard classification dataset.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Wolf Kienzle
    • 1
  • Bernhard Schölkopf
    • 1
  • Felix A. Wichmann
    • 2
    • 3
  • Matthias O. Franz
    • 1
  1. 1.Max-Planck Institut für biologische Kybernetik, Abteilung Empirische Inferenz, Spemannstr. 38, 72076 Tübingen 
  2. 2.Technische Universität Berlin, Fakultät IV, FB Modellierung Kognitiver, Prozesse, Sekr. FR 6-4, Franklinstr. 28/29, 10587 Berlin 
  3. 3.Bernstein Center for Computational Neuroscience, Philippstr. 13 Haus 6, 10115 Berlin 

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