###### Abstract  
We present a review of methods for automatic estimation of visual saliency: the perceptual property that makes specific elements in a scene stand out and grab the attention of the viewer. We focus on domains that are especially recent and relevant, as they make saliency estimation particularly useful and/or effective: omnidirectional images, image groups for co-saliency, and video sequences. For each domain, we perform a selection of recent methods, we highlight their commonalities and differences, and describe their unique approaches. We also report and analyze the datasets involved in the development of such methods, in order to reveal additional peculiarities of each domain, such as the representation used for the ground truth saliency information (scanpaths, saliency maps, or salient object regions). We define domain-specific evaluation measures, and provide quantitative comparisons on the basis of common datasets and evaluation criteria, highlighting the different impact of existing approaches on each domain. We conclude by synthesizing the emerging directions for research in the specialized literature, which include novel representations for omnidirectional images, inter- and intra- image saliency decomposition for co-saliency, and saliency shift for video saliency estimation. Keywords:co-saliency; omnidirectional images; video saliency; visual saliency estimation +
Footnote †: journal: Medical  
both in the neuroscience community and, more recently, in the computer vision and image processing communities. Due to the different levels of involved complexity, bottom-up saliency estimation methods are generally faster than top-down methods [9], and thus useful in applications where real-time feedback is considered more important than reaching higher accuracy. For example, in a live augmented reality scenario, fast saliency estimation would locate image regions deemed important for further localized computer vision analysis, and would provide precious information to avoid covering areas of potential interest with the rendering of augmentation elements. Conversely, top-down saliency estimation methods tend to be more robust, at the cost of a higher demand for computational resources. These are therefore typically employed in applications with looser time constraints, and which benefit from semantic interpretation. For example, a system for storing and organizing personal photos could exploit saliency estimation to detect objects of interests based on visual composition, and their reoccurring presence in multiple photos. In general, visual saliency estimation has been successfully employed in multiple tasks, such as image retargeting [10], video summarization [11], and photo-collage creation [12]. It has also been adopted as an intermediate pre-processing step for other computer-vision problems, such as scene recognition [13], object detection [14] and segmentation [15]. Since the advent and diffusion of deep-learning, many of these problems have been reformulated in an end-to-end fashion that does not rely on explicitly estimating the salient component, as proven by state of the art solutions in each field [16; 17; 18]. There exist, however, problems that remain directly related to the evaluation of saliency information such as advertisement assessment [19], and domains where its explicit computation is particularly relevant, for example in reducing the computational effort for analysis of large quantities of data (such as video sequences, or high-resolution panoramic images exploited in the virtual reality domain). By analyzing the recent scientific literature on saliency estimation, in fact, specific topics emerged as persistently reoccurring amidst works dedicated to saliency on regular images, due to a combination of the excellent results already reached by the scientific community, and the paradigm shift in solving certain problems without explicitly modeling general-purpose saliency.