  We present a co-segmentation technique for space-time co-located image
collections. These prevalent collections capture various dynamic events,
usually by multiple photographers, and may contain multiple co-occurring
objects which are not necessarily part of the intended foreground object,
resulting in ambiguities for traditional co-segmentation techniques. Thus, to
disambiguate what the common foreground object is, we introduce a
weakly-supervised technique, where we assume only a small seed, given in the
form of a single segmented image. We take a distributed approach, where local
belief models are propagated and reinforced with similar images. Our technique
progressively expands the foreground and background belief models across the
entire collection. The technique exploits the power of the entire set of image
without building a global model, and thus successfully overcomes large
variability in appearance of the common foreground object. We demonstrate that
our method outperforms previous co-segmentation techniques on challenging
space-time co-located collections, including dense benchmark datasets which
were adapted for our novel problem setting.
