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

MPG-Autoren
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Kienzle,  W
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

Externe Ressourcen

https://link.springer.com/content/pdf/10.1007%2F978-3-540-74936-3_41.pdf
(Verlagsversion)

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Zitation

Kienzle, W., Schölkopf, B., Wichmann, F., & Franz, M. (2007). How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye movements. In A. Hamprecht, C. Schnörr, & B. Jähne (Eds.), Pattern Recognition: 29th DAGM Symposium, Heidelberg, Germany, September 12-14, 2007 (pp. 405-414). Berlin, Germany: Springer.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-0013-CBE3-B
Zusammenfassung
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 emphlearning 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.