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  Visual saliency re-visited: Center-surround patterns emerge as optimal predictors for human fixation targets

Wichmann, F., Kienzle, W., Schölkopf, B., & Franz, M. (2008). Visual saliency re-visited: Center-surround patterns emerge as optimal predictors for human fixation targets. Poster presented at 8th Annual Meeting of the Vision Sciences Society (VSS 2008), Naples, FL, USA.

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 Creators:
Wichmann, FA, Author           
Kienzle, W1, 2, Author           
Schölkopf, B1, 2, Author           
Franz, MO, 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: Humans perceives the world by directing the center of gaze from one location to another via rapid eye movements, called saccades. In the period between saccades the direction of gaze is held fixed for a few hundred milliseconds (fixations). It is primarily during fixations that information enters the visual system. Remarkably, however, after only a few fixations we perceive a coherent, high-resolution scene despite the visual acuity of the eye quickly decreasing away from the center of gaze: This suggests an effective strategy for selecting saccade targets.

Top-down effects, such as the observer's task, thoughts, or intentions have an effect on saccadic selection. Equally well known is that bottom-up effects-local image structure-influence saccade targeting regardless of top-down effects. However, the question of what the most salient visual features are is still under debate. Here we model the relationship between spatial intensity patterns in natural images and the response of the saccadic system using tools from machine learning. This allows us to identify the most salient image patterns that guide the bottom-up component of the saccadic selection system, which we refer to as perceptive fields. We show that center-surround patterns emerge as the optimal solution to the problem of predicting saccade targets. Using a novel nonlinear system identification technique we reduce our learned classifier to a one-layer feed-forward network which is surprisingly simple compared to previously suggested models assuming more complex computations such as multi-scale processing, oriented filters and lateral inhibition. Nevertheless, our model is equally predictive and generalizes better to novel image sets. Furthermore, our findings are consistent with neurophysiological hardware in the superior colliculus. Bottom-up visual saliency may thus not be computed cortically as has been thought previously.

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 Dates: 2008-05
 Publication Status: Issued
 Pages: -
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 Identifiers: DOI: 10.1167/8.6.635
BibTex Citekey: 5437
 Degree: -

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Title: 8th Annual Meeting of the Vision Sciences Society (VSS 2008)
Place of Event: Naples, FL, USA
Start-/End Date: 2008-05-08 - 2008-05-13

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Title: Journal of Vision
Source Genre: Journal
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Publ. Info: Charlottesville, VA : Scholar One, Inc.
Pages: - Volume / Issue: 8 (6) Sequence Number: - Start / End Page: 635 Identifier: ISSN: 1534-7362
CoNE: https://pure.mpg.de/cone/journals/resource/111061245811050