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dc.contributor.authorPagès Zamora, Alba Maria
dc.contributor.authorCabrera-Bean, Margarita
dc.contributor.authorDiaz Vilor, Carles
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2018-11-14T15:09:55Z
dc.date.available2018-11-14T15:09:55Z
dc.date.issued2019-02-01
dc.identifier.citationPagès-Zamora, A., Cabrera, M., Diaz, C. Unsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis. "Pattern recognition", 1 Febrer 2019, vol. 86, p. 209-223.
dc.identifier.issn0031-3203
dc.identifier.urihttp://hdl.handle.net/2117/124243
dc.description© <2018>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstractCrowdsourced data in science might be severely error-prone due to the inexperience of annotators participating in the project. In this work, we present a procedure to detect specific structures in an image given tags provided by multiple annotators and collected through a crowdsourcing methodology. The procedure consists of two stages based on the Expectation–Maximization (EM) algorithm, one for clustering and the other one for detection, and it gracefully combines data coming from annotators with unknown reliability in an unsupervised manner. An online implementation of the approach is also presented that is well suited to crowdsourced streaming data. Comprehensive experimental results with real data from the MalariaSpot project are also included.
dc.format.extent15 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Reconeixement de formes
dc.subject.lcshPattern recognition systems
dc.subject.lcshContracting out -- Technological innovations
dc.subject.otherCrowdsourcing
dc.subject.otherUnreliable annotators
dc.subject.otherUnsupervised method
dc.subject.otherOnline EM algorithm
dc.subject.otherMalariaSpot
dc.titleUnsupervised online clustering and detection algorithms using crowdsourced data for malaria diagnosis
dc.typeArticle
dc.subject.lemacReconeixement de formes (Informàtica)
dc.subject.lemacContractació externa -- Innovacions tecnològiques
dc.contributor.groupUniversitat Politècnica de Catalunya. SPCOM - Grup de Recerca de Processament del Senyal i Comunicacions
dc.identifier.doi10.1016/j.patcog.2018.09.001
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0031320318303200
dc.rights.accessOpen Access
drac.iddocument23515701
dc.description.versionPreprint
upcommons.citation.authorPagès-Zamora, A., Cabrera, M., Diaz, C.
upcommons.citation.publishedtrue
upcommons.citation.publicationNamePattern recognition
upcommons.citation.volume86
upcommons.citation.startingPage209
upcommons.citation.endingPage223


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Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 3.0 Spain