Biologically plausible saliency mechanisms improve feedforward object recognition

The biological plausibility of statistical inference and learning, tuned to the statistics of natural images, is investigated. It is shown that a rich family of statistical decision rules, confidence measures, and risk estimates, can be implemented with the computations attributed to the standard neurophysiological model of V1. In particular, different statistical quantities can be computed through simple re-arrangement of lateral divisive connections, non-linearities, and pooling. It is then shown that a number of proposals for the measurement of visual saliency can be implemented in a biologically plausible manner, through such re-arrangements. This enables the implementation of biologically plausible feedforward object recognition networks that include explicit saliency models. The potential of combined attention and recognition is illustrated by replacing the first layer of the HMAX architecture with a saliency network. Various saliency measures are compared, to investigate whether (1) saliency can substantially benefit visual recognition and (2) the benefits depend on the specific saliency mechanisms implemented. Experimental evaluation shows that saliency does indeed enhance recognition, but the gains are not independent of the saliency mechanisms. Best results are obtained with top-down mechanisms that equate saliency to classification confidence.