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Unsupervised image ranking
2009
Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining - LS-MMRM '09
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. Our approach is unsupervised and allows for various feature modalities.
doi:10.1145/1631058.1631074
dblp:conf/mm/HorsterSRW09
fatcat:anvxwshxnbfvhbochtjezngjbm