  When watching omnidirectional images (ODIs), subjects can access different
viewports by moving their heads. Therefore, it is necessary to predict
subjects' head fixations on ODIs. Inspired by generative adversarial imitation
learning (GAIL), this paper proposes a novel approach to predict saliency of
head fixations on ODIs, named SalGAIL. First, we establish a dataset for
attention on ODIs (AOI). In contrast to traditional datasets, our AOI dataset
is large-scale, which contains the head fixations of 30 subjects viewing 600
ODIs. Next, we mine our AOI dataset and determine three findings: (1) The
consistency of head fixations are consistent among subjects, and it grows
alongside the increased subject number; (2) The head fixations exist with a
front center bias (FCB); and (3) The magnitude of head movement is similar
across subjects. According to these findings, our SalGAIL approach applies deep
reinforcement learning (DRL) to predict the head fixations of one subject, in
which GAIL learns the reward of DRL, rather than the traditional human-designed
reward. Then, multi-stream DRL is developed to yield the head fixations of
different subjects, and the saliency map of an ODI is generated via convoluting
predicted head fixations. Finally, experiments validate the effectiveness of
our approach in predicting saliency maps of ODIs, significantly better than 10
state-of-the-art approaches.
