A statistical mixture method to reveal bottom-up and top-down factors guiding the eye-movements

When people gaze at real scenes, their visual attention is driven both by a set of bottom-up processes coming from the signal properties of the scene and also from top-down effects such as the task, the affective state, prior knowledge, or the semantic context. The context of this study is an assessment of manufactured objects (here car cab interior). From this dedicated context, this work describes a set of methods to analyze the eye-movements during the visual scene evaluation. But these methods can be adapted to more general contexts. We define a statistical model to explain the eye fixations measured experimentally by eye-tracking even when the ratio signal/noise is bad or lacking of raw data. One of the novelties of the approach is to use complementary experimental data obtained with the “Bubbles” paradigm. The proposed model is an additive mixture of several a priori spatial density distributions of factors guiding visual attention. The “Bubbles” paradigm is adapted here to reveal the semantic density distribution which represents here the cumulative effects of the top-down factors. Then, the contribution of each factor is compared depending on the product and on the task, in order to highlight the properties of the visual attention and the cognitive activity in each situation

A computational saliency model integrating saccade programming

International audienceSaliency models have showed the ability of predicting where human eyes fixate when looking at images. However, few models are interested in saccade programming strategies. We proposed a biologically-inspired model to compute image saliency maps. Based on these saliency maps, we compared three different saccade programming models depending on the number of programmed saccades. The results showed that the strategy of programming one saccade at a time from the foveated point best matches the experimental data from free viewing of natural images. Because saccade programming models depend on the foveated point where the image is viewed at the highest resolution, we took into account the spatially variant retinal resolution. We showed that the predicted eye fixations were more effective when this retinal resolution was combined with the saccade programming strategies