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

2007

Conference Paper

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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 emph{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.

Author(s): Kienzle, W. and Schölkopf, B. and Wichmann, F. and Franz, MO.
Book Title: Pattern Recognition
Journal: Pattern Recognition: 29th DAGM Symposium
Pages: 405-414
Year: 2007
Month: September
Day: 0
Editors: FA Hamprecht and C Schn{\"o}rr and B J{\"a}hne
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/978-3-540-74936-3_41
Event Name: 29th Annual Symposium of the German Association for Pattern Recognition (DAGM 2007)
Event Place: Heidelberg, Germany

Address: Berlin, Germany
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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BibTex

@inproceedings{4486,
  title = {How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye movements},
  author = {Kienzle, W. and Sch{\"o}lkopf, B. and Wichmann, F. and Franz, MO.},
  journal = {Pattern Recognition: 29th DAGM Symposium},
  booktitle = {Pattern Recognition},
  pages = {405-414},
  editors = {FA Hamprecht and C Schn{\"o}rr and B J{\"a}hne},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2007},
  month_numeric = {9}
}