Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
This work addresses the need for more accurate clutter metrics in vision research, particularly for applications in human-computer interaction and visual perception studies, though it is incremental as it builds on existing models.
The authors tackled the problem of predicting clutter's detrimental effect on target search in complex scenes by introducing a foveated clutter model, which improved correlation with target detection hit rates from r(44) = -0.19 to r(44) = -0.82 compared to a non-foveated baseline.
Previous studies have proposed image-based clutter measures that correlate with human search times and/or eye movements. However, most models do not take into account the fact that the effects of clutter interact with the foveated nature of the human visual system: visual clutter further from the fovea has an increasing detrimental influence on perception. Here, we introduce a new foveated clutter model to predict the detrimental effects in target search utilizing a forced fixation search task. We use Feature Congestion (Rosenholtz et al.) as our non foveated clutter model, and we stack a peripheral architecture on top of Feature Congestion for our foveated model. We introduce the Peripheral Integration Feature Congestion (PIFC) coefficient, as a fundamental ingredient of our model that modulates clutter as a non-linear gain contingent on eccentricity. We finally show that Foveated Feature Congestion (FFC) clutter scores r(44) = -0.82 correlate better with target detection (hit rate) than regular Feature Congestion r(44) = -0.19 in forced fixation search. Thus, our model allows us to enrich clutter perception research by computing fixation specific clutter maps. A toolbox for creating peripheral architectures: Piranhas: Peripheral Architectures for Natural, Hybrid and Artificial Systems will be made available.