@inproceedings{3950,
  title = {Learning an Interest Operator from Human Eye Movements},
  journal = {Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006)},
  booktitle = {CVPWR 2006},
  abstract = {We present an approach for designing interest operators that are
  based on human eye movement statistics. In contrast to existing
  methods which use hand-crafted saliency measures, we use machine
  learning methods to infer an interest operator directly from eye
  movement data. That way, the operator provides a measure of
  biologically plausible interestingness. We describe the data
  collection, training, and evaluation process, and show that our
  learned saliency measure significantly accounts for human eye
  movements. Furthermore, we illustrate connections to existing
  interest operators, and present a multi-scale interest point
  detector based on the learned function.},
  pages = {page 24},
  editors = {C Schmid and S Soatto and C Tomasi},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = apr,
  year = {2006},
  slug = {3950},
  author = {Kienzle, W. and Wichmann, FA. and Sch{\"o}lkopf, B. and Franz, MO.},
  month_numeric = {4}
}