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Crowdclustering

Gomes, Ryan and Welinder, Peter and Krause, Andreas and Perona, Pietro (2011) Crowdclustering. Computation & Neural Systems Technical Report, CNS-TR. California Institute of Technology , Pasadena, CA. (Submitted) http://resolver.caltech.edu/CaltechAUTHORS:20110628-202526159

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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 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 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.


Item Type:Report or Paper (Technical Report)
Additional Information:This work was supported by ONR MURI grant N00014-06-1-0734 and NSF grant IIS-0953413.
Group:Computation & Neural Systems Technical Reports
Funders:
Funding AgencyGrant Number
Office of Naval Research (ONR) Multidisciplinary University Research Initiative (MURI) N00014-06-1-0734
NSFIIS-0953413
Record Number:CaltechAUTHORS:20110628-202526159
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20110628-202526159
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:24244
Collection:CaltechAUTHORS
Deposited By: Ryan Gomes
Deposited On:29 Jun 2011 21:29
Last Modified:26 Dec 2012 13:21

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