Unsupervised image ranking
Eva Hörster, Malcolm Slaney, Marc’Aurelio Ranzato, Kilian Q. Weinberger · 2009
In the paper, we propose and test an unsupervised approach for image ranking. Prior solutions are based on image content and the similarity graph connecting images. We generalize this idea by directly estimating the likelihood of each photo in a feature space. We hypothesize the photos at the peaks of this distribution are the most likely photos for any given category and therefore these images are the most representative.