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Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data




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.

Author(s): Wichmann, FA. and Kienzle, W. and Schölkopf, B. and Franz, M.
Journal: Journal of Vision
Volume: 9
Number (issue): 8
Pages: article 32
Year: 2009
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1167/9.8.32

Links: Web


  title = {Non-linear System Identification: Visual Saliency Inferred from Eye-Movement Data},
  author = {Wichmann, FA. and Kienzle, W. and Sch{\"o}lkopf, B. and Franz, M.},
  journal = {Journal of Vision},
  volume = {9},
  number = {8},
  pages = {article 32},
  year = {2009}