Salient object detection has a wide range of applications in computer vision tasks. Although tremendous progress has been made in recent decades, the weak light image still poses formidable challenges to current saliency models due to its low illumination and low signal-to-noise ratio properties. Traditional hand-crafted features inevitably encounter great difficulties in handling images with weak light backgrounds, while most of the high-level features are unfavorable to highlight visually salient objects in weak light images. In allusion to these problems, an optimal feature selection-guided saliency seed propagation model is proposed for salient object detection in weak light images. The main idea of this paper is to hierarchically refine the saliency map by learning the optimal saliency seeds in weak light images recursively. Particularly, multiscale superpixel segmentation and entropy-based optimal feature selection are first introduced to suppress the background interference. The initial saliency map is then obtained by the calculation of global contrast and spatial relationship. Moreover, local fitness and global fitness are used to optimize the prediction saliency map. Extensive experiments on six datasets show that our saliency model outperforms 20 state-of-the-art models in terms of popular evaluation criteria.

中文翻译：

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通过最优特征选择引导的种子传播在弱光图像中进行显着性检测

显着物体检测在计算机视觉任务中具有广泛的应用。尽管近几十年来取得了巨大的进步，但由于其低照度和低信噪比特性，弱光图像仍然对当前的显着性模型构成了巨大的挑战。传统的手工特征在处理弱光背景图像时不可避免地遇到很大困难，而大多数高级特征不利于在弱光图像中突出视觉上显着的物体。针对这些问题，提出了一种最优特征选择引导的显着性种子传播模型，用于弱光图像中的显着目标检测。本文的主要思想是通过递归地学习弱光图像中的最优显着性种子来分层细化显着性图。特别，首先引入多尺度超像素分割和基于熵的最优特征选择来抑制背景干扰。然后通过计算全局对比度和空间关系得到初始显着图。此外，局部适应度和全局适应度用于优化预测显着图。在六个数据集上的大量实验表明，就流行的评估标准而言，我们的显着性模型优于 20 个最先进的模型。