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Advances in Multimedia Modeling: 15th International by Benoit Huet, Alan F. Smeaton, Ketan Mayer-Patel, Yannis

By Benoit Huet, Alan F. Smeaton, Ketan Mayer-Patel, Yannis Avrithis

This publication constitutes the refereed lawsuits of the fifteenth overseas Multimedia Modeling convention, MMM 2009, held in Sophia-Antipolis, France, in January 2009. The 26 revised complete papers and 20 revised poster papers offered including 2 invited talks have been conscientiously reviewed and chosen from a hundred thirty five submissions. The papers are geared up in topical sections on computerized annotation, coding and streaming, video semantics and relevance, audio, acceptance, type and retrieval, in addition to question and summarization.

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Sub-Tree Fusion merges estimations from immediate children at s. Let x ˆ(s|sai ) be the optimal estimate at one of children sai of node s and vsai , the sub-tree rooted at sai , and P (s|sai ) for the corresponding error covariance. Ks x ˆ(s|s−) = P (s|s−) P −1 (s|sai )ˆ x(s|sai ) (15) i=1 P −1 (s|s−) = Px−1 (s) + Ks [P −1 (s|sai ) − Px−1 (s)] (16) i=1 Error covariance matrix P (s|sai ) indicates the distribution of attention weight on salient features at the given resolution. This matrix is kept for the later coarse-to-fine smoothing.

In order to organise the thousands of images generated per day, the physical location of the user for each image captured could potentially be very useful as additional metadata. For example, if you had lost your briefcase, your SenseCam could help you automatically retrace your steps. Similarly, at a conference it could be used to establish what posters you had visited, providing the basis for determining where you spent your time (and thus by implication what you found most interesting). Of course, a number of technological solutions are available for localisation, such as GPS [1], GSM [2] and RF-based localisation using base-station signal strength [3].

Modern information retrieval. ACM Press, New York (1999) 15. : Bipartite graph reinforcement model for web image annotation. ACM Multimedia, 585–594 (2007) 16. : Image retrieval by hypertext links. SIGIR, 296–303 (1997) 17. : Giving meaning to web images. ACM Multimedia, 39–47 (2000) 18. : Structure-sensitive manifold ranking for video concept detection. ACM MM, 23–29 (2007) 19. : Web image annotation by fusing visual features and textual information. In: SAC, pp. 1056–1060 (2007) 20. : Region-based image annotation using asymmetrical support vector machine-based multiple-instance learning.

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