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Learning an Interest Operator from Human Eye Movements

W Kienzle, FA Wichmann, B Schölkopf, and MO Franz


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.


Publication typeInproceedings
Published inCVPWR 2006
Pagespage 24
AddressLos Alamitos, CA, USA
PublisherIEEE Computer Society
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