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Abstract:
Is it possible to crowdsource categorization? Amongst the challenges: (a) each worker has only a partial view of the data, (b) different workers may have differ- ent clustering criteria and may produce different numbers of categories, (c) the underlying category structure may be hierarchical. We propose a Bayesian model of how workers may approach clustering and show how one may infer clusters / categories, as well as worker 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.
Reference:
Crowdclustering R. Gomes, P. Welinder, A. Krause, P. PeronaIn Proc. Neural Information Processing Systems (NIPS), 2011
Bibtex Entry:
@inproceedings{gomes11crowd,
	Author = {Ryan Gomes and Peter Welinder and Andreas Krause and Pietro Perona},
	Booktitle = {Proc. Neural Information Processing Systems (NIPS)},
	Title = {Crowdclustering},
	Year = {2011}}