Foveated Analysis and Selection of Visual Fixations in Natural Scenes

Abstract

The ability to automatically detect visually interesting regions in images has practical applications in the design of active machine vision systems. Analysis of the statistics of image features at observers gaze can provide insights into the mechanisms of fixation selection in humans. Using a novel foveated analysis framework, in which features were analyzed at the spatial resolution at which they were perceived, we studied the statistics of four low-level local image features: luminance, contrast, center-surround outputs of luminance and contrast, and discovered that the image patches around human fixations had, on average, higher values of each of these features than the image patches selected at random. Center-surround contrast showed the greatest difference between human and random fixations, followed by contrast, center-surround luminance, and luminance. Using these measurements, we present a new algorithm that selects image regions as likely candidates for fixation. These regions are shown to correlate well with fixations recorded from observers.