Adaptive Multimedia Retrieval: User, Context, and Feedback: by Stefan Rüger (auth.), Marcin Detyniecki, Joemon M. Jose, PDF

By Stefan Rüger (auth.), Marcin Detyniecki, Joemon M. Jose, Andreas Nürnberger, C. J. van Rijsbergen (eds.)

This e-book is a longer selection of revised contributions that have been at first submitted to the overseas Workshop on Adaptive Multimedia Retrieval (AMR 2005). This workshop was once geared up in the course of July 28-29, 2005, on the U- versity of Glasgow, united kingdom, as a part of a knowledge retrieval study pageant and in co-location with the nineteenth overseas Joint convention on Arti?cial Int- ligence (IJCAI 2005). AMR 2005 used to be the 3rd and up to now the largest occasion of the sequence of workshops that began in 2003 with a workshop through the twenty sixth German convention on Arti?cial Intelligence (KI 2003) and persevered in 2004 as a part of the sixteenth eu convention on Arti?cial Intelligence (ECAI 2004). Theworkshopfocussedespeciallyonintelligentmethodstoanalyzeandstr- ture multimedia collections, with specific awareness on equipment which are in a position to help the person within the seek strategy, e. g. , via delivering extra user-and context-adapted information regarding the hunt effects in addition to the knowledge coll- tion itself and particularly through adapting the retrieval device to the user’s wishes and pursuits. The invited contributions offered within the ?rst portion of this e-book— “Putting the consumer within the Loop: visible source Discovery” from Stefan Rug ¨ er, “Using Relevance suggestions to Bridge the Semantic hole” from Ebroul Izquierdo and Divna Djordjevic, and “Leveraging Context for Adaptive Multimedia - trieval: an issue of keep watch over” from Gary Marchionini—illustrate those middle t- ics: user,contextandfeedback. Theseaspectsarediscussedfromdi?erent issues ofviewinthe18contributionsthatareclassi?edintosixmainchapters,following relatively heavily the workshop’s classes: score, structures, spatio-temporal re- tions, utilizing suggestions, utilizing context and meta-data.

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Additional info for Adaptive Multimedia Retrieval: User, Context, and Feedback: Third International Workshop, AMR 2005, Glasgow, UK, July 28-29, 2005, Revised Selected Papers

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This approach is based on the assumption that all positive examples have similar feature vectors and can be cluster together in feature space. Heuristic techniques based on feature re-weighting and query point movement use empiric parameter adaptation annealing well-established weighting methods in textbased retrieval. In this model, features are weighted differently according to their classification power. The classification power is derived from the analysis of relevant images fed back by the user.

Content Engineering. Electronics Communications Engineering Journal, Vol. 14, No. 4, Aug. 2002 2. , Figueiredo, M. A. , Jain, A. -J. Image classification for content-based indexing. IEEE Trans. Image Processing, 2001, 10, pp. 117-130 34 E. Izquierdo and D. Djordjevic 3. , Jain, A. : On image classification: city vs. landscape. Proc. IEEE Workshop on Content-Based Access of Image and Video Libraries, 1998, pp. 3-8 4. Dorai C. and S. : Bridging the semantic gap with computational media aesthetics.

The idea behind these methods is to infer semantic classes from low-level descriptors using well-established recognition techniques [2]. These techniques use pattern recognition on manually labelled content to train the system. g. landscape, cityscape [3]. The aim is to link visual primitives with few semantic classes and to use these few generic high-level descriptions to annotate the images in the database. This bottom-up approach, from low-level to semantic classification, mostly relies on matching procedures and pattern recognition at the lowest level of content interpretation.

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