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

Nan Mu,Jun Yang,Hongyu Han,Hongyu Wang,Yu Zhang,Liang Zou

doi:10.1155/2021/9921831

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.

Highlights

Aiming to mimic human visual system (HVS), which has the ability to effortlessly sort out the most attractive things from the scene in front of eyes, the goal of salient object detection is to calculate the most important objects in an image
A large number of bottom-up and top-down salient object detection models have been proposed, most of them are only designed for normal light scenes. ese saliency models are confronted with significant challenges in weak light images due to low signal-to-noise ratio and lack of well-defined features to capture saliency information in low lighting scenarios. e most likely reasons may attribute to two aspects: (1) current hand-crafted visual features can hardly evaluate the objectness in weak light images; (2) most of the high-level features normally present enormous challenges in detecting accurate object boundary information, which can be blurred due to multiple levels of convolution layers and pooling layers in common convolutional neural network models
On MSRA, DUT-OMRON, and PASCAL-S datasets ((a), (d) and (e) of Figures 3 and 4 and Tables 1–3), our model achieves the best performance on the TPRs-FPRs curve, PR curve, and area under the curve (AUC) score, while the saliency model saliency optimization (SO) obtains the best mean absolute error (MAE) score and weighted F-measure (WF) score, and the saliency model MIL obtains the best overlapping ratio (OR) score. e main reason is that the SO model used boundary connectivity and global optimization to increase its robustness, and the MIL model introduced a multiple-instance learning approaches to

Summary

Introduction

Aiming to mimic human visual system (HVS), which has the ability to effortlessly sort out the most attractive things from the scene in front of eyes, the goal of salient object detection is to calculate the most important objects in an image. E most likely reasons may attribute to two aspects: (1) current hand-crafted visual features can hardly evaluate the objectness in weak light images; (2) most of the high-level features normally present enormous challenges in detecting accurate object boundary information, which can be blurred due to multiple levels of convolution layers and pooling layers in common convolutional neural network models. To address these challenges, this paper proposes an optimal feature selection-based saliency seed propagation model for salient object detection in weak light images Several hand-crafted visual features are selected to hierarchically refine the saliency map obtained from the high-level cues recursively. e flowchart of our model is presented in Figure 1. e optimal low-level features are first selected to give a robust expression for weak light images, Input image

G Feature extraction

Related Works

Experimental Setup

Conclusion