Stochastic Attentional Selection and Shift on the Visual Attention Pyramid

Abstract

This paper proposes a computational model of visual attention which performs stochastic attentional selection and shift on the visual attention pyramid that is computed for each image frame of a video sequence. In this model, the visual attention pyramid is generated according to the rareness criteria by using intensity contrast, saturation contrast, hue contrast, orientation and motion energy on a Gaussian resolution pyramid. On this attention pyramid, stochastic attentional selection and shift is performed on mechanisms of the dynamic maintenance of IOR(Inhibition Of Return), the bottom-up spatial attention and the adaptive competitive filtering of attention. Experimental results show that this model achieves stochastic visual pop-out to artificial pop-out targets and stochastic attentional selection and shift, especially the whole-part attention shift and the motion-follow attention, in daily scenes