ML Anthology

Learning to Predict Where Humans Look

Tilke Judd, Krista A. Ehinger, Frédo Durand, Antonio Torralba
ICCV 2009 pp. 2106-2113
doi:10.1109/ICCV.2009.5459462 /iccv/2009/judd2009iccv-learning/

Abstract

For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Where eye tracking devices are not a viable option, models of saliency can be used to predict fixation locations. Most saliency approaches are based on bottom-up computation that does not consider top-down image semantics and often does not match actual eye movements. To address this problem, we collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing examples to learn a model of saliency based on low, middle and high-level image features. This large database of eye tracking data is publicly available with this paper.

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Cite

Text

Judd et al. "Learning to Predict Where Humans Look." IEEE/CVF International Conference on Computer Vision, 2009. doi:10.1109/ICCV.2009.5459462

Markdown

[Judd et al. "Learning to Predict Where Humans Look." IEEE/CVF International Conference on Computer Vision, 2009.](https://mlanthology.org/iccv/2009/judd2009iccv-learning/) doi:10.1109/ICCV.2009.5459462

BibTeX

@inproceedings{judd2009iccv-learning,
  title     = {{Learning to Predict Where Humans Look}},
  author    = {Judd, Tilke and Ehinger, Krista A. and Durand, Frédo and Torralba, Antonio},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2009},
  pages     = {2106-2113},
  doi       = {10.1109/ICCV.2009.5459462},
  url       = {https://mlanthology.org/iccv/2009/judd2009iccv-learning/}
}