The human visual system can recognize several thousand object categories irrespective of their position and size. This combination of selectivity and invariance is built up gradually across several stages of visual processing. However, the recognition of multiple objects in cluttered visual scenes presents a difficult problem for human as well as machine vision systems. The human visual system has evolved to perform two stages of visual processing: a pre-attentive parallel processing stage, in which the entire visual field is processed at once and a slow serial attentive processing stage, in which aregion of interest in an input image is selected for "specialized" analysis by an attentional spotlight. We argue that this strategy evolved to overcome the limitation of purely feed forward processing in the presence of clutter and crowding. Using a Bayesian model of attention along with a hierarchical model of feed forward recognition on a data set of real world images, we show that this two stage attentive processing can improve recognition in cluttered and crowded conditions.
