Communal Cuts: sharing cuts across images

Identifying objects that are common to a set of images is an important step in unsupervised image analysis. While existing approaches to object extraction and co-segmentation commonly focus merely on joint per-class appearance models, we propose a model that incorporates additional label and boundary co-occurrence information with little additional communication across images. This model admits efficient inference and can improve the accurate extraction of difficult, fine-structured objects.