Unsupervised clustering of clickthrough data for automatic annotation of multimedia content
Authors: Ntalianis, Klimis 
Doulamis, Anastasios 
Tsapatsoulis, Nicolas 
Doulamis, Nikolaos 
Issue Date: 27-Nov-2009
Conference: 19th International Confence on Artificial Neural Networks (ICANN 2009), 14-17 September 2009, Limassol, Cyprus 
Book: Artificial Neural Networks - ICANN 2009 
Series: Lecture Notes in Computer Science
Keywords: Automatic annotation of multimedia, Clickthrough data, Image retrieval
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.
ISBN: 978-3-642-04277-5
978-3-642-04276-8
ISSN: 1611-3349
DOI: 10.1007/978-3-642-04277-5_90
URI: https://uniwacris.uniwa.gr/handle/3000/2859
Type: Conference Paper
Department: Department of Business Administration 
School: School of Administrative, Economics and Social Sciences 
Affiliation: University of West Attica (UNIWA) 
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου

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