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Πεδίο DCΤιμήΓλώσσα
dc.contributor.authorTsapatsoulis, Nicolasen
dc.contributor.authorTheodosiou, Zenonas-
dc.contributor.otherΤσαπατσούλης, Νικόλαςen
dc.contributor.otherΘεοδοσίου, Ζήνωνας-
dc.date.accessioned2013-02-13T14:02:53Zen
dc.date.accessioned2013-05-17T10:05:00Z-
dc.date.accessioned2015-12-09T10:48:21Z-
dc.date.available2013-02-13T14:02:53Zen
dc.date.available2013-05-17T10:05:00Z-
dc.date.available2015-12-09T10:48:21Z-
dc.date.issued2011en
dc.identifier.citationIEEE 5th international conference on internet multimedia systems architecture and application (IMSAA), 2011, Bangalore, 12-13 Decemberen
dc.identifier.urihttps://hdl.handle.net/20.500.14279/4004-
dc.description.abstractTagging large collections is often prohibitive and manual tags are known to be imprecise, ambiguous, inconsistent and subject to many variations. A possible way to alleviate these problems and improve the annotation quality is to obtain multiple annotations per image by assigning several annotators into the task. In the current work we present an approach to model the view of several annotators using four MPEG-7 descriptors and a well known data classifier. We apply keywords modelling to the annotation data collected in the framework of Commandaria project where sixteen non-expert users annotated a set of a hundred images using a predefined set of keywords. The images sharing a common keyword are grouped together and used for the creation of the visual model corresponds to this keyword. Finally, the created models used to classify the images into the keyword classes in terms of 2-classes combinations using the 10-fold cross-validation technique. The experimental results are examined under two perspectives: First, in terms of the separation ability of the various keyword classes and second, in terms of the efficiency of the four visual descriptors as far as the image classification task is concerneden
dc.formatpdfen
dc.language.isoenen
dc.rights© 2011 IEEEen
dc.subjectInterneten
dc.subjectMultimedia systemsen
dc.titleCrowdsourcing annotation: modelling keywords using low level featuresen
dc.typeConference Papersen
dc.collaborationCyprus University of Technology-
dc.subject.categoryArts-
dc.countryCyprus-
dc.subject.fieldComputer and Information Sciences-
dc.identifier.doi10.1109/IMSAA.2011.6156351en
dc.dept.handle123456789/100en
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.deptDepartment of Communication and Marketing-
crisitem.author.deptDepartment of Communication and Internet Studies-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.orcid0000-0002-6739-8602-
crisitem.author.orcid0000-0003-3168-2350-
crisitem.author.parentorgFaculty of Communication and Media Studies-
crisitem.author.parentorgFaculty of Communication and Media Studies-
Εμφανίζεται στις συλλογές:Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation
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