2pm, Thursday, 30 November 2006
A Nonparametric Approach to Bottom-Up Visual Saliency
Wolf Kienzle, Max Planck Institute for Biological Cybernetics
This talk addresses the bottom-up influence of local image
information on human eye movements. Most existing computational
models use a set of biologically plausible linear filters, e.g.,
Gabor or Difference-of-Gaussians filters as a front-end, the
outputs of which are nonlinearly combined into a real number that
indicates visual saliency. Unfortunately, this requires many
design parameters such as the number, type, and size of the
front-end filters, as well as the choice of nonlinearities,
weighting and normalization schemes etc., for which biological
plausibility cannot always be justified. As a result, these
parameters have to be chosen in a more or less ad hoc way. Here,
we propose to learn a visual saliency model directly from
human eye movement data. The model is rather simplistic and
essentially parameter-free, and therefore contrasts recent
developments in the field that usually aim at higher prediction
rates at the cost of additional parameters and increasing model
complexity. Experimental results show that---despite the lack of
any biological prior knowledge---our model performs comparably to
existing approaches, and in fact learns image features that
resemble findings from several previous studies. In particular,
its maximally excitatory stimuli have center-surround structure,
similar to receptive fields in the early human visual system.
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