A Biologically Plausible Model for Rapid Natural Scene Identification

Contrast statistics of the majority of natural images conform to a Weibull distribution. This property of natural images may facilitate efficient and very rapid extraction of a scene's visual gist. Here we investigated whether a neural response model based on the Weibull contrast distribution captures visual information that humans use to rapidly identify natural scenes. In a learning phase, we measured EEC activity of 32 subjects viewing brief Hashes of 700 natural scenes. From these neural measurements and the contrast statistics of the natural image stimuli, we derived an across subject Weibull response model. We used this model to predict the HKG responses to 100 new natural scenes and estimated which scene the subject viewed by finding the best match between the mode) predictions and the observed EEG responses. In almost 90 percent of the cases our model accurately predicted the observed scene. Moreover, in most failed cases, the scene mistaken for the observed scene was visually similar to the observed scene itself. Similar results were obtained in a separate experiment in which 16 other subjects where presented with artificial occlusion models of natural images. Together, these results suggest that Weibull contrast statistics of natural images contain a considerable amount of visual gist information to warrant rapid image identification.