## 3 Visual Saliency Estimation
### Domains  
The scientific literature on saliency estimation has witnessed the emergence of domain-specific solutions, covering a wide range of topics that go beyond the traditional regular-image input. Specifically, recent developments have shown several works in the domains of omnidirectional images, image groups for co-saliency estimation, and video sequences, as exemplified in Figure 1. Other fields of application, such as light field [25] and hyper-spectral imaging [26; 27] have also caught the attention of saliency-related research. Visual saliency estimation is, however, still at its early stages in such domains, and a full review of related methods is therefore left as a future development. Another relevant domain of research is depth-assisted visual saliency estimation, which explores the advantages of predicting saliency on so-called RGB-D images [28]. In this case, the additional  
Figure 1: Visualization of the three domains of interest for visual saliency estimation: omnidirectional images (**a**), image groups for co-saliency (**b**), and clips for video saliency (**c**). knowledge associated to the distance between the camera and the depicted elements can improve the separation of subjects from the background, providing a precious piece of information for better saliency estimation. Despite the clear relevance of the topic, we chose not to explicitly discuss this domain since such a wide field deserves a whole dedicated survey paper. Nonetheless, we found that depth-assisted saliency estimation is sometimes included in the analyzed domains of omnidirectional images [29], co-saliency [30], and video saliency [31]. We will, therefore, reference and discuss only these works in the corresponding sections. **Omnidirectional** images (ODIs) are panoramic representations of a scene, covering a 360\({}^{\circ}\)solid angle from a single viewpoint, typically employed in passive virtual reality. Virtual reality is the experience of a simulated world, which can be navigated by the user to varying degrees of freedom [32]. In a passive virtual reality scenario, the spatial movements are predefined, and the virtual environment is precomputed in a sequence of omnidirectional images. During fruition, the user only determines the direction of viewing, i.e., the subpart of the ODI to visualize at any given time. Image cropping for thumbnail selection is particularly valuable when operating on large omnidirectional images, depicting wide sceneries in high-resolution [33]. Storing and transmitting these ODIs can then benefit from perceptually-aware compression, i.e., reducing the represented detail over areas that are considered \"less-interesting\" [34]. Image **co-saliency** refers to the problem of estimating the saliency from a group of images that depict the same subject. The rationale behind this approach is to provide the saliency estimation model with additional information, and thus partially compensate for the ill-posed nature of the problem. Depending on the chosen level of abstraction, image groups for co-saliency estimation could either represent exactly the same instance from multiple points of view, or different instances of the same category, possibly characterized by slight variations in appearance. **Video** saliency is the task of performing saliency estimation on a sequence of frames. By considering the time component, in fact, estimation of visual saliency acquires additional value in terms of understanding how people react to, and learn from images [35]. If we exclude the naive frame-by-frame approach, multiple-frame analysis helps a given model gain a global view of the input, in a fashion similar to what happens with co-saliency estimation. In addition, the annotated sequences are expected to exhibit different patterns compared to single image saliency, as the vision of each single video frame is both limited in time and highly influenced by the previous frames.