# Review of Visual Saliency Prediction: Development Process from Neurobiological Basis to Deep Models
## 3 Classic Visual Saliency Models
### Bottom-Up Visual Saliency Models  
Bottom-up visual saliency models usually extract low-level features, such as contrast, color, and texture. The difference between low-level features and background features strongly attract attention resources. This attention prediction mechanism is involuntary and entails fast processing. For example, the presence of pedestrians, vehicles, individual flowers, and beasts in an image will show strong visual saliency. Among them, the local contrast model is based on the physiological and psychological principles of FIT and the center surround antagonism, and it defines a certain mechanism when selecting salient areas in an image to realize the simulation of the visual attention mechanism. For example, the earliest model of [PERSON] [4] could simulate the process of shifting human visual attention without any prior information. According to the features captured from images, the model analyzed visual stimuli, allocated computing resources, selected the salient areas in the scene according to the saliency intensity of different positions, and simulated the process of human visual attention transfer. Although the performance of the model was general, it was the first successful attempt from the neurobiological model, which is of great significance. Since then, other researchers have contributed improvements. [PERSON] [28] changed the graph-based visual saliency (GBVS) model to the Markov random field with non-linear combination in the synthesis stage. The model formed activation maps on certain feature channels, and then normalized them in a way which highlighted conspicuity and admitted combination with other maps. [PERSON] and [PERSON] [29] used local contrast analysis to extract the saliency maps of an image, and on this basis, [PERSON] et al. [30] used 9 \(\times\) 9 neighborhoods and adopted a conditional random field (CRF) learning model. [PERSON] [31] analyzed local rarity based on the sparse coding. [PERSON] et al. [32] proposed a saliency prediction model based on Boolean Map. In addition, researchers have used other models to predict saliency by using local or global contrast. Some of the notable examples include the pixel-level contrast saliency model proposed by [PERSON] and [PERSON] [33], the sliding windows-based model for global contrast calculation proposed by [PERSON] [34], the color contrast linear fusion model proposed by [PERSON] [35], the frequency tuning model proposed by [PERSON] [36], and the color space quantization model proposed by [PERSON] [37]. Other researchers have used the superpixel [38; 39; 40] as the processing unit to calculate the variance of color space distribution as a means of improving the computational efficiency. Some models have been based on information theory and image transformation. The essence of these models based on information theory is to calculate the maximum information sampling from the visual environment, select the richest part from the scene, and discard the remaining part. Among them, the Attention-based on Information Maximization (AIM) model of [PERSON] and [PERSON] [41] was influential. The AIM model has used [PERSON]'s self-information measure to calculate the saliency of the image. Firstly, a certain number of natural image blocks were randomly selected for training to obtain the basic function. Then, the image was divided into blocks of the same size, the basis coefficients of the corresponding blocks were extracted as the features of the block through Independent Component Analysis (ICA), the distribution of each feature was obtained through probability density estimation, and finally the probability density of the feature was obtained. Other notable models included the incremental coding length model proposed by [PERSON] [42], the rare linear combination model proposed by [PERSON] [43], the self-similarity prediction model proposed by [PERSON] [44] and the Mahalanobis distance calculation model proposed by [PERSON] [45].