The Contribution of Statistical Image Differences to Human Rapid Categorization of Natural Scenes is Negligible

V. Maljkovic, P. Martini and H. Farid

Vision Sciences (VSS), Sarasota, FL, 2006

Purpose: To examine the contribution of low-level image properties to the rapid categorization of natural scenes.

Methods:. Three image classes were tested in a blocked design: positive/negative emotional images, landscapes/cityscapes and animals/vehicles. Within each category, half of the images were natural, whereas the remaining were synthetic stimuli generated by matching statistical feature vectors extracted from the same image class (Portilla & Simoncelli, 2000) and lacked any meaning. Each image was presented (masked) for 13-50msec, once per subject. Natural and synthetic images were shown mixed within a block of trials. 24 subjects categorized both natural and synthetic images into their natural class within a 2AFC design and accuracy of categorization was calculated per exposure.

Results: All categories of natural images were reliably discriminated after a single video frame. Categorization of synthetic images, however, was impaired at all exposures. Each class of natural images was discriminated far more accurately than the corresponding synthetic images: 93% vs. 67% for cityscapes/landscapes, 94% vs. 56% for animals/vehicles, and 70% vs. 53% for emotional images (averages across exposures, chance 50%).

Conclusions: The contribution of statistical image differences to image categorization at brief exposures is small in general. It is more sizeable for image categories such as animal/vehicles and cityscapes/landscapes where computational linear discriminant analysis algorithms have some success. In the case of emotional image categorization the contribution is virtually null, matching the failure of computational algorithms (Maljkovic et al., VSS 2004). Thus, scene categorization at brief exposures seems not to overly rely on low-level image statistics.