受韦伯定理和协作式中心环绕接收域生物模型的启发,提出一种适用于图像显著性检测的中心环绕假设,设计了一种具有圆形拓扑的中心环绕结构,并给出基于该模型的融合局部和全局特性的显著性检测算法。该算法提取各像素的中心环绕结构,以基于韦伯特性的梯度方向表征局部显著性,中心区域相对于总体均值的相对亮度差表征全局显著性,然后采用线性合并方法得到最终的显著图。检测结果及正确率-召回率评估曲线表明该算法具有良好的检测特性,并且在激活区域具有强响应,同时还能很好地抑制其他区域。

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.