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  A Nonparametric Approach to Bottom-Up Visual Saliency

Kienzle, W., Wichmann, F., Schölkopf, B., & Franz, M. (2007). A Nonparametric Approach to Bottom-Up Visual Saliency. Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference, 689-696.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-CBCF-C Version Permalink: http://hdl.handle.net/21.11116/0000-0002-E1F8-8
Genre: Conference Paper

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 Creators:
Kienzle, W1, 2, Author              
Wichmann, FA1, 2, Author              
Schölkopf, B1, 2, Author              
Franz, MO1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: 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 emphlearn 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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 Dates: 2007-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: BibTex Citekey: 4147
 Degree: -

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Title: Twentieth Annual Conference on Neural Information Processing Systems (NIPS 2006)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2006-12-04 - 2006-12-07

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Title: Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference
Source Genre: Journal
 Creator(s):
Schölkopf, B1, Editor            
Platt, JC, Editor
Hoffman, T, Editor
Affiliations:
1 Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 689 - 696 Identifier: ISBN: 0-262-19568-2