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Learning an Interest Operator from Human Eye Movements

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons84012

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84314

Wichmann,  FA
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

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

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83919

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

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Zitation

Kienzle, W., Wichmann, F., Schölkopf, B., & Franz, M. (2006). Learning an Interest Operator from Human Eye Movements. Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW 2006), 24-24.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-D23B-5
Zusammenfassung
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.