Benchmarking human visual search computational models in natural scenes: models comparison and reference datasets

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.

Modeling human visual search: A combined Bayesian searcher and saliency map approach for eye movement guidance in natural scenes

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.