Dynamic visual selective attention model

We propose a new biologically motivated dynamic bottom-up selective attention model, which can generate a saliency map (SM) by considering dynamics of continuous input scenes as well as saliency of the primitive features of a static input scene. The maximum entropy algorithm is used to develop the dynamic selective attention model, whereby the input consists of the static bottom-up SMs for the successive static scenes. The experimental results show that the proposed model can generate more plausible scan paths for a dynamic scene compared with those obtained by the static bottom-up attention model.