一种鲁棒的夜间图像显著性对象检测模型

Robust Salient Object Detection Model for Nighttime Images

基于人类视觉注意机制的显著性对象检测模型作为能主动感知图像中重要信息的有效方法,对探索视觉早期认知过程的大范围知觉信息组织具有重要意义.然而,由于夜间图像具有低信噪比和低对比度特性,现有的视觉显著性对象检测模型在夜间场景中容易受到噪声干扰、弱纹理模糊等多方面因素的影响.有鉴于此,提出一种基于区域协方差和全局搜索的夜间图像显著性对象检测方法.首先,将输入图像分割为超像素块,并分别计算它们的协方差.然后,使用超像素块协方差的差异性作为适应度函数,并结合全局搜索算法来优化各个超像素块的显著值.最后,通过图扩散方法来精炼显著图结果.实验测试采用了5个公开图像数据集和1个夜间图像数据集,通过与11种目前主流的视觉显著性对象检测模型进行对比,综合评价了所提出模型的性能.

Human visual attention based saliency model is an effective way for the active perception of important information in image,and it can play an important role for exploring large-scale perceptual information organization in the early stage of visual cognitive process. However,nighttime images usually have low signal-to-noise ratio and low contrast properties. Conventional salient object detection models may face great challenges in this scenario,such as noise influence,and weak texture blur. This paper proposes a region covariance and global search based salient object detection model for nighttime images. The input image is firstly divided into superpixel regions to estimate their covariance. Then,covariance feature based local saliency and global search based image saliency can be calculated respectively. Finally,a graph-based diffusion process is performed to refine the saliency maps. Extensive experiments have been conducted to evaluate the performance of the proposed method against eleven state-of-the-art models on five benchmark datasets and a nighttime image dataset.