dc.contributor.author	Jagadeesan, A. P.	en_US
dc.contributor.author	Wenzel, J.	en_US
dc.contributor.author	Corney, Jonathan R.	en_US
dc.contributor.author	Yan, X.	en_US
dc.contributor.author	Sherlock, A.	en_US
dc.contributor.author	Torres-Sanchez, C.	en_US
dc.contributor.author	Regli, William	en_US
dc.contributor.editor	Mohamed Daoudi and Tobias Schreck	en_US
dc.date.accessioned	2013-10-21T16:10:02Z
dc.date.available	2013-10-21T16:10:02Z
dc.date.issued	2010	en_US
dc.description.abstract	Although a significant number of benchmark data sets for 3D object based retrieval systems have been proposed over the last decade their value is dependent on a robust classification of their content being available. Ideally researchers would want hundreds of people to have classified thousands of parts and the results recorded in a manner that explicitly shows how the similarity assessments varies with the precision used to make the judgement. This paper reports a study which investigated the proposition that Internet Crowdsourcing could be used to quickly and cheaply provide benchmark classifications of 3D shapes. The collective judgments of the anonymous workers produce a classification that has surprisingly fine granularity and precision. The paper reports the results of validating Crowdsourced judgements of 3D similarity against Purdue's ESB and concludes with an estimate of the overall costs associated with large scale classification tasks involving many tens of thousands of models.	en_US
dc.description.seriesinformation	Eurographics Workshop on 3D Object Retrieval	en_US
dc.identifier.isbn	978-3-905674-22-4	en_US
dc.identifier.issn	1997-0471	en_US
dc.identifier.uri	https://doi.org/10.2312/3DOR/3DOR10/055-062	en_US
dc.publisher	The Eurographics Association	en_US
dc.title	Fast Human Classification of 3D Object Benchmarks	en_US

Fast Human Classification of 3D Object Benchmarks

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