# Salient Object Detection via Fusion of Multi-Visual Perception
## 2 Related Work  
Over the past two decades, salient object detection methods have developed rapidly. Vision-based object detection is an interdisciplinary research topic in image processing, computer vision, and pattern recognition [4]. Some strategies for salient object detection use unsupervised techniques by combining visual features and prior theory. The earliest creative method proposed by [PERSON] et al. [20] was based on color, intensity, and contrast around the center of the orientation. Then, [PERSON] et al. [21] introduced a regional-level saliency descriptor primarily constructed on local-contrast, background, and other well-known features. [PERSON] et al. [22] described a frequency-adjusted global-contrast-based method to measure saliency. [PERSON] et al. [23] used a graph-based approach to measure the similarity of regions to foreground or background cues through the boundary prior and manifold ordering. [PERSON] and [PERSON] [24] calculated the saliency scores for each region and then compared them to the soft foreground and background. [PERSON] et al. [25] utilized a Bayesian framework for saliency detection based on a Bayesian model. To overcome the limitation of using only contrast, [PERSON] et al. [26] proposed the use of statistical and color contrast to detect salient objects. Furthermore, there are some successful examples salient object detection by combining visual features with prior theory [27; 28; 29].  
With the boom in deep learning [12], saliency detection has introduced supervised learning methods. Among them, it is worth mentioning the region-based CNN model and the FCN-based (fully convolutional networks) model. Region-based methods [14] divide the input image into multi-scale or smaller regions and use CNNs to extract their high-level features. Then, the multi-layer perceptron (MLP) outputs the saliency value of each small region through these high-level features. Although region-based CNN models have achieved good performance, these models cannot preserve spatial information due to small region segmentation. Therefore, an FCN-based method was designed to overcome this shortcoming [13]. This method operates at the pixel level rather than at the region or patch level. It overcomes the limitations of region-based CNN models and also preserves contextual information well. However, substantial labeled data with teacher signals or ground truth is necessary for their training, leading to inherent computational complexity and potential unavailability.  
The traditional methods mentioned above typically focus on a limited set of features, such as local contrast and color contrast, resulting in under-utilization of image information. Deep learning methods typically extract semantic features through convolution, neglecting surface-level features of images. Additionally, they perform convolution at the block level, overlooking the consideration of connectivity between salient objects across blocks. Furthermore, they require a large amount of annotated data, which is time-consuming and labor-intensive, and the complexity of networks results in high time complexity.  
To address the aforementioned challenges, this paper proposes a multi-feature detection method leveraging the rich features of images. Employing multiple features is not only beneficial for saliency detection in complex natural scenes but also mitigates limitations of single-feature approaches. Moreover, the proposed bottom-up method is simple and data-independent.