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Updated: Dec 6, 2025
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Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
Published on: November 14, 2018
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Can we accurately predict where we look at paintings?
Olivier Le Meur1, Tugdual Le Pen1, Rémi Cozot2
1Univ Rennes, CNRS, IRISA, Rennes, France.
Plos One
|October 9, 2020
Summary
Researchers studied how people look at paintings, finding gaze patterns similar to natural scenes. They developed a new saliency model to accurately predict important areas in art, aiding image-based applications.
Area of Science:
- Computer Vision
- Art Analysis
- Human-Computer Interaction
Background:
- Understanding visual attention is crucial for analyzing how humans perceive images.
- Previous research has primarily focused on gaze patterns in natural scenes, with less attention to artworks.
Purpose of the Study:
- To investigate and simulate human gaze deployment on paintings.
- To develop a computational model for predicting salient areas in artworks.
Main Methods:
- Collected a large eye-tracking dataset of 150 paintings across 5 art movements.
- Evaluated existing saliency models and proposed a novel deep-based model.
Main Results:
- Gaze deployment on paintings showed significant similarity to that on natural scenes.
- The proposed saliency model substantially outperformed current deep-based models in predicting salient areas.
- Accurate prediction of salient regions in paintings was achieved.
Conclusions:
- Human visual attention mechanisms for art share similarities with those for natural environments.
- The developed saliency model offers a powerful tool for analyzing and manipulating artistic images.
- This research opens possibilities for novel image-based applications like art animation and video generation from static paintings.
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