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.
Highlights
In the human brain, the processing of visual information requires up to 30% percent of the cortex, which is by far the most important when compared with other senses, such as touch and hearing
We expect that deep learning saliency models significantly outperform traditional saliency models, even if they have been trained on natural scenes
To go deeper into the analysis, we evaluate the performances for the five styles, namely Fauvism, Impressionism, Pointillism, Realism, and Romanticism
Summary
The processing of visual information requires up to 30% percent of the cortex, which is by far the most important when compared with other senses, such as touch and hearing. Visual attention is composed of two different kinds of attention, namely overt and covert attention The former is extremely interesting in the context of this study since this form of attention involves eye-movements. The covert attention requires a volitional effort to direct our attention to a specific area of the visual field. This is clearly the case when we glance at something out of the corner of our eyes. The bottom-up attention is unconscious and does not require any conscious effort to move our gaze This means that our gaze is effortlessly drawn by some parts of our visual field, which are salient.
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