## 5 Co-Saliency
### Datasets for Co-Saliency
The number in parentheses indicates the version of the MSRC dataset identified by the number of image groups. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Stimuli**} & \multicolumn{4}{c}{**Cardinalities**} \\
**Dataset** & **Video/** & **CGI/** & **Resolution** & **Same** & **Groups** & **Images** & **Total** & **Responses** & **Project** \\  & **Image** & **Real** & **(Pixels)** & **Subject** & **Groups** & **Images** & **Total** & **Images** & **Pose** \\ \hline RGBD Coseg183 [30] & Image & Real & 640 \(\times\) 480 & Yes & 16 & \(6\div 18\) & 183 & OR & [105] \\ RGBD Cosall150 [96] & Image & Mixed & 303 \(\times\) 117 & Mixed & 21 & \(2\div 20\) & 150 & OR & [106] \\ iCoseg [98] & Image & Real & 333 \(\times\) 500 & Mixed & 38 & \(4\div 41\) & 643 & OR & [107] \\ MSRC [86] & Image & Real & 320 \(\times\) 213 & No & 14 & \(24\div 32\) & 418 & OR & [108] \\ Cosall2015 [87] & Image & Real & 93 \(\times\) 3008 & No & 50 & \(26\div 52\) & 2015 & OR & [109] \\ Cooseg-Rep [88] & Image & Real & 137 \(\times\) 1280 & No & 22+1 & \(9\div 49\) (+116) & 572 & OR & [110] \\ Internet Images [102] & Image & Real & \(107\div 340\) & No & 3 & \(561\div 1306\) & 2746 & OR & [111] \\ Image-Pair [103] & Image & Real & 66 \(\times\) 500 & Mixed & 105 & 2 & 210 & OR & [112] \\ Safari [104] & Video & Real & 270 \(\div\) 640 & Yes & 9 & \(20\div 50\) & 415 & OR & [113] \\ Viocosegment [37] & Video & Real & 216 \(\div\) 480 & Yes & 10+38 & \(18\div 40\) & 743 & OR & [114] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Selected datasets for co-saliency estimation with corresponding characteristics (OR = Object Regions). Authors of the Cosal2015 [87] dataset gathered images in challenging scenarios from the YouTube video set [115] and the ILSVRC2014 detection set [84] (ImageNet Large Scale Visual Recognition Competition), observing that images belonging to the same group often involve similar backgrounds, leading to potentially wrong co-saliency estimations. The dataset has been annotated by 20 different users, whereas most of the other reported datasets involve one human annotation per image. Coseg-Rep [88] is a dataset for co-segmentation and co-sketch, the objective being to automatically infer a common pattern from instances of the same subject. It is composed of 22 categories of different flowers and animals, plus a special \"repetitive\" category, which contains images with repeating patterns aimed at inter-image co-segmentation and co-saliency. Internet Images [102], also known as Internet Datasets, is composed of only three image groups (car, horse, and airplane), characterized however by high cardinality inside each group. It presents a total of 15,270 images, out of which 2746 are provided with a segmentation ground truth that was acquired using both the LabelMe annotation toolbox [116] and Amazon Mechanical Turk. The Safari dataset [104] is a video-based collection of annotated sequences for object co-segmentation, partially built upon the existing MOViCS dataset [117] (Multi-Object Video Co-Segmentation). It is composed of nine videos of five animal classes: for each class, there is one video sequence containing only that specific class, plus one or more videos of the class in conjunction with other classes. Vicosegment [37] is another, more recent, video dataset for co-segmentation and co-saliency. It is composed of 10 category groups containing similar foreground objects, and a total of 38 videos with cardinality ranging between 18 frames and 40 frames. This dataset was presented in conjunction with the already presented method by [PERSON] et al. based on inter-video foreground correspondence and intra-video saliency stimuli. The Image-Pair [103] dataset contains groups of only two images, depicting (at least) one common object on two different background scenes. It is composed of image pairs collected from the dataset from [PERSON] et al. [118], the Caltech-256 Object Categories database [119], and the PASCAL Visual Object Challenge dataset [120].