This paper presents a novel fixation prediction and saliency modeling framework based on
inter-image similarities and ensemble of Extreme Learning Machines (ELM). The proposed …

A large number of saliency models, each based on a different hypothesis, have been
proposed over the past 20 years. In practice, while subscribing to one hypothesis or …

A deep feature-based saliency model (DeepFeat) is developed to leverage understanding of
the prediction of human fixations. Conventional saliency models often predict the human …

Automatically predicting human eye fixations is a useful technique that can facilitate many
multimedia applications, eg, image retrieval, action recognition, and photo retargeting …

In this paper, we proposed an integrated model of both semantic-aware and contrast-aware
saliency (SCA) combining both bottom-up and top-down cues for effective eye fixation …

Recent results suggest that state-of-the-art saliency models perform far from optimal in
predicting fixations. This lack in performance has been attributed to an inability to model the …

There is an increasing interest in learning mappings from features to saliency maps based
on human fixation data on natural images. These models have achieved better results than …

Here we present DeepGaze II, a model that predicts where people look in images. The
model uses the features from the VGG-19 deep neural network trained to identify objects in …

Understanding and predicting the human visual attention mechanism is an active area of
research in the fields of neuroscience and computer vision. In this paper, we propose …

Where humans choose to look can tell us a lot about behaviour in a variety of tasks. Over the
last decade numerous models have been proposed to explain fixations when viewing still …