This paper presents a novel deep architecture for saliency prediction. Current state of the art models for saliency prediction employ Fully Convolutional networks that perform a non-linear combination of features extracted from the last convolutional layer to predict saliency maps. We propose an architecture which, instead, combines features extracted at different levels of a Convolutional Neural Network (CNN). Our model is composed of three main blocks: a feature extraction CNN, a feature encoding network, that weights low and high level feature maps, and a prior learning network. We compare our solution with state of the art saliency models on two public benchmarks datasets. Results show that our model outperforms under all evaluation metrics on the SALICON dataset, which is currently the largest public dataset for saliency prediction, and achieves competitive results on the MIT300 benchmark.
@cite_27: Previous studies of eye gaze have shown that when looking at images containing human faces, observers tend to rapidly focus on the facial regions. But is this true of other high-level image features as well? We here investigate the extent to which natural scenes containing faces, text elements, and cell phones - as a suitable control - attract attention by tracking the eye movements of subjects in two types of tasks - free viewing and search. We observed that subjects in free-viewing conditions look at faces and text 16.6 and 11.1 times more than similar regions normalized for size and position of the face and text. In terms of attracting gaze, text is almost as effective as faces. Furthermore, it is difﬁcult to avoid looking at faces and text even when doing so imposes a cost. We also found that subjects took longer in making their initial saccade when they were told to avoid faces text and their saccades landed on a non-face non-text object. We reﬁne a well-known bottom–up computer model of saliency-driven attention that includes conspicuity maps for color, orientation, and intensity by adding high-level semantic information (i.e., the location of faces or text) and demonstrate that this signiﬁcantly improves the ability to predict eye ﬁxations in natural images. Our enhanced model’s predictions yield an area under the ROC curve over 84 for images that contain faces or text when compared against the actual ﬁxation pattern of subjects. This suggests that the primate visual system allocates attention using such an enhanced saliency map.
@cite_18: 
@cite_21: 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.
Similarly, Goferman al @cite_18 present a technique which aims at identifying salient regions that are distinctive with respect to both their local and global surroundings. Similarly to Cerf al @cite_27 , Judd al @cite_21 propose an approach that combines low-level features (color, orientation and intensity) with high-level semantic information (i.e. the location of faces, cars and text) and show that this solution significantly improves the ability to predict eye fixations. In general, these approaches presented hand-tuned features or trained specific higher-level classifiers.