## 4 Omnidirectional Images
### Methods for Omnidirectional Images  
Table 1 presents a synthetic overview of recent methods for saliency estimation in omnidirectional images. All analyzed methods target a ground truth in the form of fixation saliency maps. They all produce a continuous saliency map output related to fixation data, whereas only a limited subset also explicitly predicts scanpath trajectories [34; 35]. Part of the research in this field consists of evaluating the transferability of existing methods originally designed for classical images [33; 34; 61; 63; 73]. [PERSON] et al. [33] initially collected the SVR (Saliency in Virtual Reality) dataset. Through observations on the extensive and diverse set of acquisitions, they acquired knowledge about fixation bias, which they used to improve upon existing saliency estimation solutions when applied in the field of omnidirectional images. They applied the developed method to a wide range of use cases, including automatic montage and summarization of videos, thumbnail extraction, and video compression. [PERSON] et al. [34] gathered data only relative to the whole head movement, instead of tracking the viewers' eyes, when collecting their own dataset. The authors first proposed a method to convert this information into saliency maps. They then observed a fixation bias as well, which is addressed using the proposed Fused Saliency Maps (FSM) method, operating on existing saliency estimation solutions. [PERSON] et al. [61] presented an architectural extension that can be applied to any existing neural network for saliency estimation, in order to fine-tune it to the specific domain of omnidirectional images. The underlying idea is the extraction of six undistorted patches of the panoramic view, their independent evaluation, and subsequent fusion. As previously mentioned, some methods devised specific representations of the input data, that allow for full exploitation of the domain, but without suffering from its intrinsic disadvantages \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Method** & \begin{tabular}{l} **2D-to-ODI** \\ **Adaptation** \\ \end{tabular} & \begin{tabular}{l} **Custom** \\ **Representation** \\ \end{tabular} & \begin{tabular}{l} **Backbone** \\ **CNN** \\ \end{tabular} & \
\begin{tabular}{l} **Deep** \\ **Learning** \\ \end{tabular} &
\begin{tabular}{l} **Hand-** \\ **Crafted** \\ \end{tabular} & **Target** & **Output** \\ \hline [PERSON] 2019 [29] & & & (none) & & ✓ & FM & FM \\ [PERSON] 2018 [33] & ✓ & & (none) & & ✓ & FM & FM \\ [PERSON] 2018 [61] & ✓ & & VGG\_CNN\_M & ✓ & & FM & FM \\ [PERSON] 2018 [62] & & & (none) & & ✓ & FM & FM \\ [PERSON] 2018 [63] & ✓ & & (none) & & ✓ & FM & FM \\ [PERSON] 2018 [36] & & ✓ & ResNet-50/VGG-16 & ✓ & & FM & FM \\ [PERSON] 2018 [64] & & & (none) & & ✓ & FM & FM \\ [PERSON] 2017 [34] & ✓ & & (none) & & ✓ & FM & FM, SP \\ [PERSON] 2017 [35] & & ✓ & VGG-16 & ✓ & & FM, SP & FM, SP \\ [PERSON] 2017 [73] & ✓ & ✓ & (none) & & ✓ & FM & FM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of recent methods for visual saliency estimation in omnidirectional images. The “Target” column indicates the nature of the ground truth used to train or develop the methods, while “Output” describes what data they are explicitly able to generate (FM = Fixation maps, SP = Scanpaths). (namely, image distortions) [36; 39; 73]. [PERSON] et al. [35] propose a novel representation, called saliency volume, to extract saliency information that can be adapted to different forms: from the time-independent saliency maps, to the ordered scanpaths (extracted through specific sampling strategies), to a hybrid representation, which consists in temporally weighted saliency maps. [PERSON] et al. [36], who also collected the Wild-360 dataset, presented a weakly-supervised training for a spatial-temporal neural network architecture.