@inproceedings{4486,
  title = {How to Find Interesting Locations in Video: A Spatiotemporal Interest Point Detector Learned from Human Eye movements},
  journal = {Pattern Recognition: 29th DAGM Symposium},
  booktitle = {Pattern Recognition},
  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 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.},
  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},
  author = {Kienzle, W. and Sch{\"o}lkopf, B. and Wichmann, F. and Franz, MO.},
  doi = {10.1007/978-3-540-74936-3_41},
  month_numeric = {9}
}