  This paper addresses the problem of unsupervised object localization in an
image. Unlike previous supervised and weakly supervised algorithms that require
bounding box or image level annotations for training classifiers in order to
learn features representing the object, we propose a simple yet effective
technique for localization using iterative spectral clustering. This iterative
spectral clustering approach along with appropriate cluster selection strategy
in each iteration naturally helps in searching of object region in the image.
In order to estimate the final localization window, we group the proposals
obtained from the iterative spectral clustering step based on the perceptual
similarity, and average the coordinates of the proposals from the top scoring
groups. We benchmark our algorithm on challenging datasets like Object
Discovery and PASCAL VOC 2007, achieving an average CorLoc percentage of 51%
and 35% respectively which is comparable to various other weakly supervised
algorithms despite being completely unsupervised.
