## 7 Conclusions  
We presented a survey on visual saliency estimation, by focusing on recent developments in domains that are not restricted to the traditional single-image input. Adequately modeling the process of visual saliency has been shown to be particularly useful and/or effective in specific cases, such as omnidirectional images employed in virtual reality scenarios, image groups depicting the same subject for co-saliency estimation, and finally video sequences for video saliency estimation.  
Omnidirectional images, in particular, are the most recently-introduced domain for saliency. Many different methods in the analyzed literature approached the problem by developing novel representations of the input data, in a form that does not introduce, or that prevents, image distortions which might negatively impact the saliency estimation process. An evaluation of methods that are directly comparable showed that hand-crafted solutions present excellent results in this particular domain. Co-saliency estimation exploits the concept of image groups to partially constrain the ambiguous concept of visual saliency. Recent methods in this domain are focusing on the independent estimation of intra-image saliency (the traditional concept of image saliency) and inter-image saliency (finding common elements among images in the same group), and their subsequent combination. Direct comparison showed the apparent superiority of deep learning solutions for this specific domain. Video sequences offer yet another example of leveraging multiple pieces of input data to facilitate the saliency estimation process. The nature of ground truth data is inherently different from that of the traditional domain, as the viewer's attention can move to different elements in the short or long term. This phenomenon is called \"saliency shift\", and it has been explicitly addressed by recent methods in the field.  
The ground truth information for visual saliency can be collected in different forms and levels of abstraction: scanpaths (directly related to eye-gaze trajectories), continuous saliency maps, and binary salient object regions. The datasets involved in recent methods for saliency estimation have been described, among other criteria, in terms of their ground truth nature. Datasets composed of omnidirectional images are provided with either scanpaths or saliency maps, i.e., no binary segmentation masks are provided. Conversely, all analyzed datasets for co-saliency are manually annotated in terms of binary salient objects, without the use of eye tracking devices. Finally, the domain of video saliency offers a heterogeneous scenario, with many datasets offering ground truth data at all levels of abstraction.  
As a general observation that covers all analyzed domains, it is worth noting that a well-balanced distribution persists, between traditional hand-crafted algorithms and deep learning methods, among recent solutions for the problem of visual saliency estimation.  
In conclusion, this work complements existing state of the art analyses that mainly focuses on regular images. We integrated such studies with a review on saliency estimation for omnidirectional images, image groups, and video sequences. A natural extension of this work is to develop a thorough analysis of emerging topics such as light field saliency and hyper-spectral saliency, as well as widely-explored domains such as depth-assisted visual saliency estimation.  
**Funding:** The research leading to these results has received funding from TEINVEIN: TEnologie INnovative per i VEicoli Intelligenti, CUP (Codice Unico Progetto - Unique Project Code): E96 DI7000110009 - Call \"Accordi per la Ricerca e I'Innovazione\", cofunded by POR FESR 2014-2020 (Programma Operativo Regionale, Fondo Europeo di Sviluppo Regionale--Regional Operational Programme, European Regional Development Fund).  
**Conflicts of Interest**: The author declares no conflict of interest.