Adaptive Multimedia Retrieval: Retrieval, User, and by Josip Krapac, Frédéric Jurie (auth.), Nozha Boujemaa, Marcin

By Josip Krapac, Frédéric Jurie (auth.), Nozha Boujemaa, Marcin Detyniecki, Andreas Nürnberger (eds.)

This e-book constitutes the completely refereed post-workshop complaints of the fifth overseas Workshop on Adaptive Multimedia Retrieval, AMR 2007, held in Paris, France, in July 2007.

The 18 revised complete papers provided including 2 invited papers have been conscientiously chosen in the course of rounds of reviewing and development. The papers are geared up in topical sections on photo annotation, suggestions and consumer modelling, track retrieval, fusion, P2P and middleware, databases and summarization, in addition to ontology and semantics.

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Extra resources for Adaptive Multimedia Retrieval: Retrieval, User, and Semantics: 5th International Workshop, AMR 2007, Paris, France, July 5-6, 2007 Revised Selected Papers

Example text

Intuitively, if uIq (Tj ) is high, this is an evidence that Tj is a good candidate for annotating Iq . Details on how the steady state probabilities can be efficiently computed even for large graphs can be found in [16]. In Figure 4 an example of RWR navigation is shown. In particular, the Gq graph displayed in Figure 3 is navigated (with k = 1) by starting from the query image Iq and crossing nodes R16 , R8 , R13 , I3 , T2 , I1 , R3 , respectively. 3 Limits of MMG Even if MMG with RWR is usually able to find some relevant terms for annotating a query image, it suffers some limits.

Pattern Anal. Mach. Intell. 22(12), 1349–1380 (2000) 16. : Fast Random Walk with Restart and Its Applications.

3877, pp. 222–235. Springer, Heidelberg (2006) 2. : A Sound Algorithm for Region-Based Image Retrieval Using an Index. In: Proceedings of the 4th International Workshop on Query Processing and Multimedia Issue in Distributed Systems (QPMIDS 2000), Greenwich, London, UK, September 2000, pp. 930–934 (2000) 3. : FeedbackBypass: A New Approach to Interactive Similarity Query Processing. In: Proceedings of the 27th International Conference on Very Large Data Bases (VLDB 2001), Rome, Italy, September 2001, pp.

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