  Finding objects is essential for almost any daily-life visual task. Saliency
models have been useful to predict fixation locations in natural images, but
are static, i.e., they provide no information about the time-sequence of
fixations. Nowadays, one of the biggest challenges in the field is to go beyond
saliency maps to predict a sequence of fixations related to a visual task, such
as searching for a given target. Bayesian observer models have been proposed
for this task, as they represent visual search as an active sampling process.
Nevertheless, they were mostly evaluated on artificial images, and how they
adapt to natural images remains largely unexplored.
  Here, we propose a unified Bayesian model for visual search guided by
saliency maps as prior information. We validated our model with a visual search
experiment in natural scenes recording eye movements. We show that, although
state-of-the-art saliency models perform well in predicting the first two
fixations in a visual search task, their performance degrades to chance
afterward. This suggests that saliency maps alone are good to model bottom-up
first impressions, but are not enough to explain the scanpaths when top-down
task information is critical. Thus, we propose to use them as priors of
Bayesian searchers. This approach leads to a behavior very similar to humans
for the whole scanpath, both in the percentage of target found as a function of
the fixation rank and the scanpath similarity, reproducing the entire sequence
of eye movements.
