一种生物启发的视觉显著模型  

A Bioinspired Visual Saliency Model

作　　者：张珂[1] 赵歆波[2] 莫蓉[1] ZHANG Ke;ZHAO Xinbo;MO Rong(Key Laboratory of Contemporary Design and Integrated Manufacturing Technology of Ministry of Education,Northwestern Polytechnical University,Xi′an 710072,China;School of Computer Science,Northwestern Polytechnical University,Xi′an 710129,China)

机构地区：[1]西北工业大学现代设计与集成制造技术教育部重点实验室,陕西西安710072 [2]西北工业大学计算机学院,陕西西安710129

出　　处：《西北工业大学学报》2019年第3期503-508,共6页Journal of Northwestern Polytechnical University

基　　金：国家自然科学基金(61871326,61231016);陕西省自然科学基础研究计划(2018JM6116)资助

摘　　要：提出一种生物启发式的视觉显著模型,利用初级视皮层中的end-stopping机制来提取角点、边缘交叉点和线段端点等这些表征潜在显著物体轮廓的特征,将其与亮度、颜色和方向特征合并形成最终显著图。模型模拟了视觉信号从视网膜、侧膝体到初级视皮层的处理机制:首先根据视网膜和侧膝体特性,将图像分解为亮度和对立颜色通道;然后通过2D Gabor滤波模拟简单细胞感受野, Gabor响应振幅用于表示复杂细胞响应;最后V1层end-stopped细胞响应通过不同朝向的复杂细胞响应乘积来计算,V1层和侧膝体的输出共同构成自底向上显著图。公开数据集上的实验结果显示模型能够准确地预测人眼注视点,性能达到了自底向上模型的先进水平。This paper presents a bioinspired visual saliency model. The end-stopping mechanism in the primary visual cortex is introduced in to extract features that represent contour information of latent salient objects such as corners, line intersections and line endpoints, which are combined together with brightness, color and orientation features to form the final saliency map. This model is an analog for the processing mechanism of visual signals along from retina, lateral geniculate nucleus(LGN)to primary visual cortex V1: Firstly, according to the characteristics of the retina and LGN, an input image is decomposed into brightness and opposite color channels;Then, the simple cell is simulated with 2D Gabor filters, and the amplitude of Gabor response is utilized to represent the response of complex cell;Finally, the response of an end-stopped cell is obtained by multiplying the response of two complex cells with different orientation, and outputs of V1 and LGN constitute a bottom-up saliency map. Experimental results on public datasets show that our model can accurately predict human fixations, and the performance achieves the state of the art of bottom-up saliency model.

关 键 词：自底向上显著模型 视觉注意力 end-stopping机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]