Crowdclustering

Part of Advances in Neural Information Processing Systems 24 (NIPS 2011)

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Authors

Ryan Gomes, Peter Welinder, Andreas Krause, Pietro Perona

Abstract

<p>Is it possible to crowdsource categorization? Amongst the challenges: (a) each annotator has only a partial view of the data, (b) different annotators may have different clustering criteria and may produce different numbers of categories, (c) the underlying category structure may be hierarchical. We propose a Bayesian model of how annotators may approach clustering and show how one may infer clusters/categories, as well as annotator parameters, using this model. Our experiments, carried out on large collections of images, suggest that Bayesian crowdclustering works well and may be superior to single-expert annotations.</p>