# Salient Object Detection via Fusion of Multi-Visual Perception
## 1 Introduction  
Attention plays an extremely important role in human perception systems [1]. Human visual perception often selectively disregards unnecessary information through attentional mechanisms. To effectively extract target information, it selectively attends to salient regions by integrating local contextual cues. The human visual system [2] is an important medium for human cognition of the world and has strong recognition and information processing capabilities. In the human visual system, humans can autonomously ignore a large amount of secondary and redundant information and further accurately locate and extract key information in the image. This key information can be perceived and refined to extract richer advanced information in the attention stage. Salient object detection imitates the human visual attention mechanism through computer calculation, with the aim of distinguishing the most visually attractive targets and quickly screen images by focusing on the target area that human eyes are most interested in [3]. Salient object detection is a binary classification problem, and its results need to highlight the boundaries and give complete targets. With the development of computer vision, this attention mechanism of the human visual system has attracted great interest. To detect more important and valuable information in images or videos, research on salient object detection is gradually emerging, with the aim of highlighting the most salient objects in an image [4]. Such research holds practical significance for applications including video surveillance, virtual reality, human-computer interaction, and autonomous navigation. Over the past few decades, many approaches have been proposed to detect salient objects [5]. In general, there are two main methods for salient object detection: bottom-up and top-down [6, 7, 8, 9]. Bottom-up approaches are fast, data-driven, and task-dependent [10] and are usually based on easy-to-implement primitive features such as color, intensity, and texture. However, salient objects cannot be detected using these features alone. Therefore, bottom-up methods make some assumptions about object and background properties, including the contrast prior, center prior, and background prior [11]. For example, the contrast prior assumes that salient regions are always different from their neighbors or scenes and can be further divided into the local contrast prior and global contrast prior [2], the center prior is established based on the fact that salient objects are more likely to exist in the center of the image, and the background prior indicates that the boundaries of the image are more likely to be part of the background. With these assumptions, salient regions can be better highlighted with background suppressed. In addition, top-down approaches are slow, well-controlled, and task-driven and require supervised learning [12] based on manually labeled training samples. With the development of deep learning methods, this method has achieved great success in salient object detection. Deep-learning-based algorithms [13; 14] can process semantic-level or image-level features of training samples and achieve the best performance for saliency detection. However, deep neural networks require a large number of labeled samples to iteratively adjust massive training parameters, have a long training process, and cannot be applied to online learning. For example, commonly used CNN (convolutional neural network) architectures, such as ResNet-50 [15] and VGG16 [16], have a large number of parameters. ResNet-50 has over 20 million parameters, while VGG16 has approximately 130 million parameters. The time required to complete a full training and inference cycle is incomparably longer than the method proposed in this paper. Therefore, most existing deep-learning-based methods are time-consuming and highly reliant on well-annotated training datasets [10]. Besides the efficient training problem, labeling a large number of samples is also a difficult task in practice [6].