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https://hdl.handle.net/20.500.14279/3557
Πεδίο DC | Τιμή | Γλώσσα |
---|---|---|
dc.contributor.author | Tsapatsoulis, Nicolas | en |
dc.contributor.author | Ntalianis, Klimis S. | en |
dc.contributor.author | Doulamis, Anastasios D. | en |
dc.contributor.other | Τσαπατσούλης, Νικόλας | - |
dc.date.accessioned | 2013-02-07T13:45:07Z | en |
dc.date.accessioned | 2013-05-17T10:11:45Z | - |
dc.date.accessioned | 2015-12-08T10:53:31Z | - |
dc.date.available | 2013-02-07T13:45:07Z | en |
dc.date.available | 2013-05-17T10:11:45Z | - |
dc.date.available | 2015-12-08T10:53:31Z | - |
dc.date.issued | 2009 | en |
dc.identifier.citation | Artificial neural networks – ICANN 2009: 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II, Pages 895-904 | en |
dc.identifier.isbn | 978-3-642-04276-8 (print) | en |
dc.identifier.issn | 978-3-642-04277-5 (online) | en |
dc.identifier.uri | https://hdl.handle.net/20.500.14279/3557 | - |
dc.description.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 | en |
dc.format | en | |
dc.language.iso | en | en |
dc.rights | © Springer Berlin Heidelberg | en |
dc.subject | Computer science | en |
dc.subject | Neural networks | en |
dc.subject | Multimedia systems | en |
dc.subject | Search engines | en |
dc.subject | Cluster analysis | en |
dc.subject | Back propagation (Artificial intelligence) | en |
dc.title | Unsupervised clustering of clickthrough data for automatic annotation of multimedia content | en |
dc.type | Book Chapter | en |
dc.collaboration | National Technical University Of Athens | - |
dc.collaboration | Technical University of Crete | - |
dc.collaboration | Cyprus University of Technology | - |
dc.subject.category | Media and Communications | - |
dc.review | peer reviewed | - |
dc.country | Cyprus | - |
dc.country | Greece | - |
dc.subject.field | Social Sciences | - |
dc.identifier.doi | 10.1007/978-3-642-04277-5_90 | en |
dc.dept.handle | 123456789/100 | en |
item.fulltext | No Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | none | - |
item.openairecristype | http://purl.org/coar/resource_type/c_3248 | - |
item.cerifentitytype | Publications | - |
item.openairetype | bookPart | - |
crisitem.author.dept | Department of Communication and Marketing | - |
crisitem.author.faculty | Faculty of Communication and Media Studies | - |
crisitem.author.orcid | 0000-0002-6739-8602 | - |
crisitem.author.parentorg | Faculty of Communication and Media Studies | - |
Εμφανίζεται στις συλλογές: | Κεφάλαια βιβλίων/Book chapters |
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