From low-level perception to high-level perception: a coherent approach for visual attention modeling

The saliency-based or bottom-up model of visual attention presented in this paper deals with still color images. The model we built is based on numerous properties of the human visual system (HVS), thus providing a biologically plausible system. The computation of early visual features such as color and orientation is a key step for any bottom-up model and the way to extract these visual features easily permits to differentiate a model from an other. The novelty of the proposed approach lies on the fact that the computation of early visual features is fully based on a HVS model consisting in projecting the picture into an opponent-colors space, applying a perceptual decomposition, contrast sensitivity and masking functions. Moreover, a strategy essentially based on a center surround mechanism and on the perceptual grouping phenomena underscores conspicuous locations by combining visual feature maps. A saliency map which is defined as a 2D topographic representation of conspicuity is then deduced. The model is applied to a number of natural images. Our results are then compared with the results of a well-know bottom-up model.