Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6873
Title: Unsupervised clustering of clickthrough data for automatic annotation of multimedia content
Authors: Tsapatsoulis, Nicolas 
Ntalianis, Klimis S. 
Doulamis, Anastasios D. 
Keywords: Computer science
Neural networks
Multimedia systems
Search engines
Cluster analysis
Back propagation (Artificial intelligence)
Issue Date: 2009
Publisher: Springer
Source: Artificial neural networks – ICANN 2009: 19th International Conference, Limassol, Cyprus, September 14-17, 2009, Proceedings, Part II, Pages 895-904
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
URI: http://ktisis.cut.ac.cy/handle/10488/6873
ISBN: 978-3-642-04276-8 (print)
ISSN: 978-3-642-04277-5 (online)
DOI: 10.1007/978-3-642-04277-5_90
Rights: © Springer Berlin Heidelberg
Appears in Collections:Κεφάλαια βιβλίων/Book chapters

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