Cosegmentation is typically defined as the task of jointly
segmenting "something similar" in a given set of images.
Existing methods are too generic and so far have not
demonstrated competitive results for any specific task. In
this paper we overcome this limitation by adding two new
aspects to cosegmentation: (1) the "something" has to be
an object, and (2) the "similarity" measure is learned. In
this way, we are able to achieve excellent results on the recently
introduced iCoseg dataset, which contains small sets
of images of either the same object instance or similar objects
of the same class. The challenge of this dataset lies in
the extreme changes in viewpoint, lighting, and object deformations
within each set. We are able to considerably outperform
several competitors. To achieve this performance,
we borrow recent ideas from object recognition: the use of
powerful features extracted from a pool of candidate objectlike
segmentations. We believe that our work will be beneficial
to several application areas, such as image retrieval.