Abstract
The topic of visual saliency is well-known and spreads across numerous disciplines. In this paper, we examine how saliency models perform on images with specific characteristics.
We explore a saliency of 14 different digitized paintings, all representing a biblical scene of The Last Supper. We evaluate the performance of different saliency models.
The models are using the traditional approach as well as the deep learning approach. For the evaluation, we use three different metrics, AUC, NSS and CC.
As ground truth, we use eye-tracking data from 35 participants. Our analysis shows that deep-learning methods predict the most salient parts of the paintings very similar to real eye fixations.
We also checked the consistency of gaze patterns among the participants.