Inspired by Weber’s Law and the biological model of synergistic center-surround receptive field

we propose a center-surround hypothesis for image saliency detection and design a center-surround structure with circular topology. Based on this model

a saliency detection method fusing local and global features is proposed. It extracts the center-surround structure of each pixel

using Weber’s Law based gradient orientation to represent local saliency

and using the relative intensity differences of the center region against the overall mean to represent global saliency

then it gives the final saliency map by linear combination of local and global saliencies. Comparison experiments and precision-recall curve demonstrate this detector has better performance

and has strong response in active region while inhibits other regions.