In this work we present a biologically motivated framework for the modelling of the visual scene exploration preference. We aim at capturing the statistical patterns that are elicited by the subjective visual selection and reproduce them via a computational system. The low level visual features are encoded through the projection of the image patches on a learned basis of linear filters reproducing the typical response properties of the primary visual cortex (V1) receptive fields of mammals. The resulting training set is typically high-dimensional and sparse. We exploit the sparse structure by clustering together patterns of channel activation which are similar on the basis of a binary activation map and finally deriving a pooling over the set of the original linear filters in terms of active (on) and non-active (off) channels for each cluster. The system has been tested on a dataset of natural images by comparing the fixation density maps recorded from human subjects observing the pictures and the saliency maps computed by our system obtaining promising results.