  Visual search is an essential part of almost any everyday human goal-directed
interaction with the environment. Nowadays, several algorithms are able to
predict gaze positions during simple observation, but few models attempt to
simulate human behavior during visual search in natural scenes. Furthermore,
these models vary widely in their design and exhibit differences in the
datasets and metrics with which they were evaluated. Thus, there is a need for
a reference point, on which each model can be tested and from where potential
improvements can be derived. In the present work, we select publicly available
state-of-the-art visual search models in natural scenes and evaluate them on
different datasets, employing the same metrics to estimate their efficiency and
similarity with human subjects. In particular, we propose an improvement to the
Ideal Bayesian Searcher through a combination with a neural network-based
visual search model, enabling it to generalize to other datasets. The present
work sheds light on the limitations of current models and how potential
improvements can be accomplished by combining approaches. Moreover, it moves
forward on providing a solution for the urgent need for benchmarking data and
metrics to support the development of more general human visual search
computational models.
