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Learning Eye Movements




The human visual system samples images through saccadic eye movements which rapidly change the point of fixation. Although the selection of eye movement targets depends on numerous top-down mechanisms, a number of recent studies have shown that low-level image features such as local contrast or edges play an important role. These studies typically used predefined image features which were afterwards experimentally verified. Here, we follow a complementary approach: instead of testing a set of candidate image features, we infer these hypotheses from the data, using methods from statistical learning. To this end, we train a non-linear classifier on fixated vs. randomly selected image patches without making any physiological assumptions. The resulting classifier can be essentially characterized by a nonlinear combination of two center-surround receptive fields. We find that the prediction performance of this simple model on our eye movement data is indistinguishable from the physiologically motivated model of Itti & Koch (2000) which is far more complex. In particular, we obtain a comparable performance without using any multi-scale representations, long-range interactions or oriented image features.

Author(s): Kienzle, W. and Wichmann, FA. and Schölkopf, B. and Franz, MO.
Journal: Sensory Coding And The Natural Environment
Volume: 2006
Pages: 1
Year: 2006
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Poster (poster)

Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Learning Eye Movements},
  author = {Kienzle, W. and Wichmann, FA. and Sch{\"o}lkopf, B. and Franz, MO.},
  journal = {Sensory Coding And The Natural Environment},
  volume = {2006},
  pages = {1},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2006},
  month_numeric = {9}