Unsupervised clustering of clickthrough data for automatic annotation of multimedia content

Ntalianis, K; Doulamis, A; Tsapatsoulis, N; Doulamis, N

dc.contributor.author	Ntalianis, K	en
dc.contributor.author	Doulamis, A	en
dc.contributor.author	Tsapatsoulis, N	en
dc.contributor.author	Doulamis, N	en
dc.date.accessioned	2014-03-01T02:46:34Z
dc.date.available	2014-03-01T02:46:34Z
dc.date.issued	2009	en
dc.identifier.issn	03029743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/32720
dc.subject	Automatic annotation of multimedia	en
dc.subject	Clickthrough data	en
dc.subject	Image retrieval	en
dc.subject.other	Automatic annotation	en
dc.subject.other	Clickthrough data	en
dc.subject.other	Content annotation	en
dc.subject.other	Controlled experiment	en
dc.subject.other	Low-level features	en
dc.subject.other	Manual annotation	en
dc.subject.other	Multimedia contents	en
dc.subject.other	Multimedia files	en
dc.subject.other	Theoretical result	en
dc.subject.other	Unsupervised clustering	en
dc.subject.other	User-dependent	en
dc.subject.other	Backpropagation	en
dc.subject.other	Cluster analysis	en
dc.subject.other	Image retrieval	en
dc.subject.other	Multimedia systems	en
dc.subject.other	Search engines	en
dc.subject.other	Neural networks	en
dc.title	Unsupervised clustering of clickthrough data for automatic annotation of multimedia content	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/978-3-642-04277-5_90	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-04277-5_90	en
heal.publicationDate	2009	en
heal.abstract	Current low-level feature-based CBIR methods do not provide meaningful results on non-annotated content. On the other hand manual annotation is both time/money consuming and user-dependent. To address these problems in this paper we present an automatic annotation approach by clustering, in an unsupervised way, clickthrough data of search engines. In particular the query-log and the log of links the users clicked on are analyzed in order to extract and assign keywords to selected content. Content annotation is also accelerated by a carousel-like methodology. The proposed approach is feasible even for large sets of queries and features and theoretical results are verified in a controlled experiment, which shows that the method can effectively annotate multimedia files. © 2009 Springer Berlin Heidelberg.	en
heal.journalName	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en
dc.identifier.doi	10.1007/978-3-642-04277-5_90	en
dc.identifier.volume	5769 LNCS	en
dc.identifier.issue	PART 2	en
dc.identifier.spage	895	en
dc.identifier.epage	904	en