Adaptive Multimedia Retrieval User Context And Feedback Third International Workshop Amr 2005 Glasgow Uk July 2829 2005 Revised Selected Papers 1st Edition Stefan Rger Auth by Stefan Rüger (auth.), Marcin Detyniecki, Joemon M. Jose, Andreas Nürnberger, C. J. Van Rijsbergen (eds.) 9783540321743, 3540321748 instant download after payment.

This book is an extended collection of revised contributions that were initially submitted to the International Workshop on Adaptive Multimedia Retrieval (AMR 2005). This workshop was organized during July 28-29, 2005, at the U- versity of Glasgow, UK, as part of an information retrieval research festival and in co-location with the 19th International Joint Conference on Arti?cial Int- ligence (IJCAI 2005). AMR 2005 was the third and so far the biggest event of the series of workshops that started in 2003 with a workshop during the 26th German Conference on Arti?cial Intelligence (KI 2003) and continued in 2004 as part of the 16th European Conference on Arti?cial Intelligence (ECAI 2004). Theworkshopfocussedespeciallyonintelligentmethodstoanalyzeandstr- ture multimedia collections, with particular attention on methods that are able to support the user in the search process, e. g. , by providing additional user-and context-adapted information about the search results as well as the data coll- tion itself and especially by adapting the retrieval tool to the user’s needs and interests. The invited contributions presented in the ?rst section of this book— “Putting the User in the Loop: Visual Resource Discovery” from Stefan Rug ¨ er, “Using Relevance Feedback to Bridge the Semantic Gap” from Ebroul Izquierdo and Divna Djordjevic, and “Leveraging Context for Adaptive Multimedia - trieval: A Matter of Control” from Gary Marchionini—illustrate these core t- ics: user,contextandfeedback. Theseaspectsarediscussedfromdi?erent points ofviewinthe18contributionsthatareclassi?edintosixmainchapters,following rather closely the workshop’s sessions: ranking, systems, spatio-temporal re- tions, using feedback, using context and meta-data.