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Journal Articles PLoS ONE Year : 2020

Can we accurately predict where we look at paintings?

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Abstract

The objective of this study is to investigate and to simulate the gaze deployment of observers on paintings. For that purpose, we built a large eye tracking dataset composed of 150 paintings belonging to 5 art movements. We observed that the gaze deployment over the proposed paintings was very similar to the gaze deployment over natural scenes. Therefore, we evaluate existing saliency models and propose a new one which significantly outperforms the most recent deep-based saliency models. Thanks to this new saliency model, we can predict very accurately what are the salient areas of a painting. This opens new avenues for many image-based applications such as animation of paintings or transformation of a still painting into a video clip.

Dates and versions

hal-03107955 , version 1 (12-01-2021)

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Olivier Le Meur, Tugdual Le Pen, Rémi Cozot. Can we accurately predict where we look at paintings?. PLoS ONE, 2020, 15 (10), pp.e0239980. ⟨10.1371/journal.pone.0239980⟩. ⟨hal-03107955⟩
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