Unsupervised Clustering of Clickthrough Data for Automatic Annotation of Multimedia Content

Authors:
Klimis Ntalianis;Anastasios Doulamis;Nicolas Tsapatsoulis;Nikolaos Doulamis
Affiliations:
Electrical and Computer Engineering Department, National Technical University of Athens, Athens, Greece 15773;Department of Production Engineering and Management, Technical University of Crete, Chania, Greece 73100;Cyprus University of Technology, Limmasol, Cyprus 3603;Electrical and Computer Engineering Department, National Technical University of Athens, Athens, Greece 15773
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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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.