There is an increasing interest in exploiting multiple images for scene understanding, with great progress in areas such as cosegmentation and video segmentation. Jointly analyzing the images in a large set offers the opportunity to exploit a greater source of information than when considering a single image on its own. However, this also yields challenges since, to effectively exploit all the available information, the resulting methods need to consider not just local connections, but efficiently analyze similarity between all pairs of pixels within and across all the images. In this paper, we propose to model an image set as a fully connected pairwise Conditional Random Field (CRF) defined over the image pixels, or superpixels, with Gaussian edge potentials. We show that this lets us co-label the images of a large set efficiently, thus yielding increased accuracy at no additional computational cost compared to sequential labeling of the images. Furthermore, we extend our framework to incorporate temporal dependence, thus effectively encompassing video segmentation as a special case of our approach, as well as to modeling label dependence over larger image regions. Our experimental evaluation demonstrates that our framework lets us handle over 10 000 images in a matter of seconds.