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Poster
Crowdclustering
Ryan G Gomes · Peter Welinder · Andreas Krause · Pietro Perona

Tue Dec 13 08:45 AM -- 02:59 PM (PST) @
in Poster Session »

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.

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Author Information

Ryan G Gomes (Caltech)
Peter Welinder (Caltech)
Andreas Krause (ETH Zurich)
Pietro Perona (California Institute of Technology)

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