August 2009
Volume 9, Issue 8
Vision Sciences Society Annual Meeting Abstract  |   August 2009
Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data
Author Affiliations
  • Felix A. Wichmann
    Technische Universität Berlin & Bernstein Center for Computational Neuroscience Berlin, Germany
  • Wolf Kienzle
    Max-Planck-Institut für biologische Kybernetik, Tübingen, Germany
  • Bernhard Schölkopf
    Max-Planck-Institut für biologische Kybernetik, Tübingen, Germany
  • Matthias Franz
    Hochschule Konstanz Technik, Wirtschaft und Gestaltung, Germany
Journal of Vision August 2009, Vol.9, 32. doi:
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      Felix A. Wichmann, Wolf Kienzle, Bernhard Schölkopf, Matthias Franz; Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data. Journal of Vision 2009;9(8):32. doi:

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      © ARVO (1962-2015); The Authors (2016-present)

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For simple visual patterns under the experimenter's control we impose which information, or features, an observer can use to solve a given perceptual task. For natural vision tasks, however, there are typically a multitude of potential features in a given visual scene which the visual system may be exploiting when analyzing it: edges, corners, contours, etc. Here we describe a novel non-linear system identification technique based on modern machine learning methods that allows the critical features an observer uses to be inferred directly from the observer's data. The method neither requires stimuli to be embedded in noise nor is it limited to linear perceptive fields (classification images). We demonstrate our technique by deriving the critical image features observers fixate in natural scenes (bottom-up visual saliency). Unlike previous studies where the relevant structure is determined manually—e.g. by selecting Gabors as visual filters—we do not make any assumptions in this regard, but numerically infer number and properties them from the eye-movement data. We show that center-surround patterns emerge as the optimal solution for predicting saccade targets from local image structure. The resulting model, a one-layer feed-forward network with contrast gain-control, is surprisingly simple compared to previously suggested saliency models. Nevertheless, our model is equally predictive. 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.

Wichmann, F. A. Kienzle, W. Schölkopf, B. Franz, M. (2009). Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data [Abstract]. Journal of Vision, 9(8):32, 32a,, doi:10.1167/9.8.32. [CrossRef] [PubMed]

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