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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.
