## 5 Co-Saliency
### Datasets for Co-Saliency  
Table 6 presents the combination of methods and datasets used in the corresponding experiments for training and evaluation. The most frequently adopted datasets are iCoseg [98] and various versions of the MSRC from Microsoft Research [86]. The latter is particularly old, the first version going back to 2005 as it was originally collected for a different purpose than saliency estimation. Different updates of the dataset have been released through the years, and the specific version is indicated in Table 6 by specifying the number of input image groups. The number of image groups is also one of the discriminating elements reported in Table 7 along with other cardinality-related information. The stimuli are described in terms of data and content type. For most reported datasets, the resolution is extremely heterogeneous across images, and it is therefore reported as a minimum-maximum side pair. The \"same subject\" column indicates whether each image group depicts exactly the same instance of the subject from different points of view, or multiple instances of the same category. All the reported co-saliency datasets provide a binary salient object region annotation, i.e., none have been collected with the aid of eye tracking devices for scanpath acquisition, relying instead on manual annotation of the contours of salient objects. RGBD Coseg183 [30] is a dataset developed for those co-saliency estimation methods that exploit the depth information associated with the input RGB image. It is partially composed of images from the RGBD Scenes Dataset [99], which were acquired using a prototype PrimeSense RGB-D camera and a firewire camera from Point Grey Research. RGBD Cosal150 [96] is a selection of images and depth maps originally coming from the RGBD NJU-1985 dataset [100] (Nanjing University), which are augmented with co-saliency pixel-level annotations. The depth information in the original dataset comes from mixed sources: either from the Kinect device used for acquisition, or inferred through an optical-flow-based method [101]. This dataset has been presented in the previously discussed method by [PERSON] et al. (2018). iCoseg [98] was collected using the \"Group\" functionality in the Flickr photography platform, in order to collect groups of images belonging to the same category (and sometimes, the same photographer), which includes various wild animals, popular landmarks, and sports teams. The authors also made available for public download the developed interface that was used to interactively annotate the dataset. MSRC [86] (from Microsoft Research) is the oldest dataset commonly used for training and evaluation of co-saliency algorithms, although originally collected for applications related to image classification. Multiple versions of the dataset exist, with the number of image groups ranging from 7 to 23. Table 7 reports information regarding the 14-groups version of the dataset. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Dataset** & **Method** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\
**2019-II [93]** & **2019** [67] & **2018** [92] & **2018** [94] & **2018** [68] & **2018** [69] & **2018** [95] & **2018** [96] & **2017-4** [37] & **2017** [70] & **2017** [89] \\ \hline RGBD Coseg183 [30] & ✓ & & & & & & & & & \\ RGBD Osall150 [96] & ✓ & & & & & & ✓ & & & ✓ & ✓ \\ iCoseg [98] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & & ✓ & ✓ \\ MSRC [96] & & ✓(8) & ✓(14) & ✓(7) & ✓(14) & ✓(8) & ✓(7) & & & ✓ (23) & ✓(7) \\ Coszl2015 [87] & & ✓ & & ✓ & ✓ & & ✓ & & ✓ & \\ Coseg-Rep [88] & & ✓ & & & & & & & ✓ & \\ Internet Images [102] & & & ✓ & & & & & & & \\ Image-Pair [103] & & & & & ✓ & & & & & \\ Safari [104] & & & & & & & & ✓ & & \\ Vicosegment [37] & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dataset/method matrix for co-saliency estimation.