# Salient Object Detection via Fusion of Multi-Visual Perception
## 1 Introduction
To tackle the problems mentioned above, this paper proposes a bottom-up-based salient object detection method using multi-visual perception. Most of the intuitive perception of human vision is unsupervised and mostly based on low-level visual features such as color, texture, and brightness. Contrast, as a very important feature for salient region recognition, has a critical impact on visual effects. The greater the contrast, the clearer and more eye-catching the image is, and the easier it is for human eyes to notice the object. The reason why salient objects can attract the attention of the human visual system is that their feature performance is different from the surrounding environment. Therefore, this paper mainly uses five different low-level visual perception features: gradient feature, mean subtractive contrast normalization (MSN) coefficients [17], the dark channel, saturation, and hue to detect salient objects by analyzing the visual presentation characteristics of natural images. More details are described in Sections 3.1 and 3.2. The method follows the feature utilization of the human visual system when recognizing objects in images. First, saliency maps are acquired by every single feature, then fused through an effective strategy, and finally combined with superpixel segmentation to obtain the final result. During the experimental phase, we utilized two challenging datasets, MSRA10K [18] and ECSSD [19], and evaluated our method using common precision-recall (PR) curve, ROC curve, F-measure, and mean absolute error (MAE) metrics. Detailed descriptions of the specific evaluation procedures are provided in Sections 5.1 and 5.2. In short, the main contributions of this paper are as follows:  
1. **Methodology for salient object detection:** The paper introduces an innovative approach to salient object detection, leveraging multi-visual perception. This pioneering method capitalizes on the synergy of various visual cues to enhance accuracy. 2. **Enhanced feature utilization:** By incorporating five distinct low-level visual features, the proposed method significantly bolsters its ability to handle intricate natural scenes. These nuanced features contribute to robustness and adaptability. 3. **Coherent framework for accurate detection:** The integration of multiple features within a coherent framework yields substantial improvements in salient object de tection. Notably, this leads to a pronounced reduction in both false positives and false negatives. In summary, our method fully utilizes the underlying representation features of images, thoroughly explores and integrates global and local characteristics, and achieves good performance without requiring costly training data or teacher signals. The remainder of this paper is organized as follows. Section 2 discusses related work, and Section 3 explains the motivation and background for our work. Section 4 presents the details of our proposed method. Then, Section 5 describes experiments demonstrating the proposed method's performance. The explanation of experimental results and further validation of the proposed method are expanded upon in Section 6. Finally, Section 7 concludes.