International Conferences And Symposiums

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

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

This ebook is a longer number of revised contributions that have been in the beginning submitted to the foreign 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 data retrieval learn competition 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 thus far the most important occasion of the sequence of workshops that all started in 2003 with a workshop in the course of the twenty sixth German convention on Arti?cial Intelligence (KI 2003) and endured in 2004 as a part of the sixteenth ecu convention on Arti?cial Intelligence (ECAI 2004). Theworkshopfocussedespeciallyonintelligentmethodstoanalyzeandstr- ture multimedia collections, with specific realization on equipment which are in a position to aid the consumer within the seek procedure, e. g. , by way of delivering extra user-and context-adapted information regarding the hunt effects in addition to the knowledge coll- tion itself and particularly by means of adapting the retrieval software to the user’s wishes and pursuits. The invited contributions provided within the ?rst component to this publication— “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 really heavily the workshop’s classes: rating, platforms, spatio-temporal re- tions, utilizing suggestions, utilizing context and meta-data.

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553-558 6. : Relevance Feedback: A Power Tool in Interactive Content-Based Image Retrieval. IEEE Tran. Circuits and Systems for Video Technology, 1998, Vol. 8, No 5, pp. 644-655 7. B. Relevance Feedback in Region-Based Image Retrieval. IEEE Transactions on Circuits and Systems for Video Technology, 2004, Vol. 14, No. 5 8. , Huan, T. , Mehrotra . S. Content-based Image Retrieval with Relevance Feedback in MARS. Proceedings of IEEE Int. Conf. on Image Processing, 1997, pp. 26-29 9. , Oja, E. Use of image Subsets in Image Retrieval with SelfOrganizing Maps.

This top-down approach puts a heavy burden on the designer of the high-level relations. Furthermore, it only delivers satisfactory results for specific application scenarios where a limited and well-defined ontology can be constructed. , top-down and bottom-up simultaneously, appear more promising. However, they are also limited to specific application scenarios were ontological structures make sense [4]. The main drawback of all these methods is that they do not consider the subjectivity of user’s interpretations.

Pixel patterns and dynamics in image and video or sampling patterns in audio signals. These signatures are called low-level descriptors and represent information that can be extracted automatically and directly from the content. Although low-level descriptors are extremely useful when a query by example is Using Relevance Feedback to Bridge the Semantic Gap 21 considered, they have little in common with high-level semantic concepts. Query by example uses similarity metrics acting on low-level features such as colour, texture, shape, motion, and audio primitives.

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