Saliency Detection in Weak Light Images via Optimal Feature Selection-Guided Seed Propagation

Abstract

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.