Please use this identifier to cite or link to this item:
|Title:||Unsupervised clustering of clickthrough data for automatic annotation of multimedia content||Authors:||Tsapatsoulis, Nicolas
Ntalianis, Klimis S.
Doulamis, Anastasios D.
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|
Show full item record
Page view(s) 502
checked on Jan 19, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.