A visual attention system, inspired by the behavior and the neuronal architecture of the early primate visual system, is used. Multiscale image features are combined into a single topographical saliency map. A dynamical neural network then selects attended locations in order of decreasing saliency. The system breaks down the complex problem of scene understanding by rapidly selecting, in a computationally efficient manner, conspicuous locations to be analyzed in detail.
Primates have a remarkable ability to interpret complex scenes in real time.
Intermediate and higher visual processes appear to select a subset of the available sensory information before further processing, most likely to reduce the complexity of scene analysis. The model used here builds on a second biologically-plausible architecture, proposed by Koch and Ullman and at the basis of several models. It is related to the so-called feature integration theory , proposed to explain human visual search strategies. Visual input is first decomposed into a set of topographic feature maps. Different spatial locations then compete for saliency within each map, such that only locations which locally stand out from their surround can persist.