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Crowdclustering

Gomes, Ryan and Welinder, Peter and Krause, Andreas and Perona, Pietro (2012) Crowdclustering. In: Advances in Neural Information Processing Systems 24 (NIPS 2011). Neural Information Processing Systems , La Jolla, CA, pp. 558-566. ISBN 9781618395993. https://resolver.caltech.edu/CaltechAUTHORS:20140527-173311294

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Abstract

Is it possible to crowdsource categorization? Amongst the challenges: (a) each worker has only a partial view of 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:Book Section
Related URLs:
URLURL TypeDescription
http://papers.nips.cc/paper/4187-crowdclustering.pdfPublisherArticle
ORCID:
AuthorORCID
Krause, Andreas0000-0001-7260-9673
Perona, Pietro0000-0002-7583-5809
Additional Information:This work was supported by ONR MURI grant 1015-G-NA-127, ARL grant W911NF-10-2-0016, and NSF grants IIS-0953413 and CNS-0932392.
Funders:
Funding AgencyGrant Number
ONR MURI1015-G-NA-127
Army Research LaboratoryW911NF-10-2-0016
NSFIIS-0953413
NSFCNS-0932392
Other Numbering System:
Other Numbering System NameOther Numbering System ID
CNS Technical ReportUNSPECIFIED
Record Number:CaltechAUTHORS:20140527-173311294
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20140527-173311294
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:45926
Collection:CaltechAUTHORS
Deposited By: Pietro Perona
Deposited On:28 May 2014 01:06
Last Modified:09 Mar 2020 13:18

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