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          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 352342.0, MPI für biologische Kybernetik / Biologische Kybernetik
A Nonparametric Approach to Bottom-Up Visual Saliency
Authors:Kienzle, W.; Wichmann, F.A.; Schölkopf, B.; Franz, M.O.
Date of Publication (YYYY-MM-DD):2007-09
Title of Proceedings:Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference
Start Page:689
End Page:696
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:This paper 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 emph{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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:4147
URL:http://books.nips.cc/papers/files/nips19/NIPS2006_...
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