This paper proposes a fast and scalable alternating optimization technique to detect
regions of interest (ROIs) in cluttered Web images without labels. The proposed
approach discovers highly probable regions of object instances by iteratively
repeating the following two functions: (1) choose the exemplar set (i.e. a
small number of highly ranked reference ROIs) across the dataset and (2) refine
the ROIs of each image with respect to the exemplar set. These two subproblems
are formulated as ranking in two different similarity networks of ROI hypotheses
by link analysis. The experiments with the PASCAL 06 dataset show that our
unsupervised localization performance is better than one of state-of-the-art techniques
and comparable to supervised methods. Also, we test the scalability of our
approach with five objects in Flickr dataset consisting of more than 200K images
