This paper aims at identifying the regions of interest in natural scenes. These regions have been defined by a behavioural measure of eye movement and by a model of saliency map constructed in a biologically plausible manner. The saliency map codes the local region of interest in terms of signal properties such as contrast, orientation, colour, Curvature etc. In our approach, pictures are processed using a retinal model, simulating the parvocellular output of the retina. The result is then filtered by a bank of Gabor filters, in mutual interaction in order to lower noise, enhance contour, and sharpen filter selectivity. Subjects' eye positions were recorded as they explored static black and white images in order to categorize these images. All fixations during one scene were averaged in order to make a density map coding the time spent for subjects on each pixel. Statistics were computed on the regions around the fixation point to evaluate an index of predictability of our saliency map. The saliency map and the density map select similar areas. Furthermore, statistics based on eye-selected regions show greater values than for randomly-selected ones.
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