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Learning an Interest Operator from Human Eye Movements

2006

Conference Paper

ei


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.

Author(s): Kienzle, W. and Wichmann, FA. and Schölkopf, B. and Franz, MO.
Book Title: CVPWR 2006
Journal: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006)
Pages: page 24
Year: 2006
Month: April
Day: 0
Editors: C Schmid and S Soatto and C Tomasi
Publisher: IEEE Computer Society

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

DOI: 10.1109/CVPRW.2006.116
Event Name: 2006 Conference on Computer Vision and Pattern Recognition Workshop
Event Place: New York, NY, USA

Address: Los Alamitos, CA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{3950,
  title = {Learning an Interest Operator from Human Eye Movements},
  author = {Kienzle, W. and Wichmann, FA. and Sch{\"o}lkopf, B. and Franz, MO.},
  journal = {Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006)},
  booktitle = {CVPWR 2006},
  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},
  month_numeric = {4}
}