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 booklet constitutes the refereed court cases of the fifteenth foreign 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 equipped in topical sections on automatic annotation, coding and streaming, video semantics and relevance, audio, reputation, type and retrieval, in addition to question and summarization.

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8]. e. the kickers) were available in the output metadata. B. Huet et al. ): MMM 2009, LNCS 5371, pp. 39–50, 2009. c Springer-Verlag Berlin Heidelberg 2009 40 T. Misu et al. However, trajectories with identities (IDs) throughout the game are indispensable if we are to produce sufficient metadata to meet queries for tactical conditions related to specific players. To obtain them, we require a robust tracking algorithm that can handle frequent occlusions. Although multiple hypothesis tracking[9] might be a solution for this, the algorithm has a drawback of difficulty in modeling/implementing graph operations.

These features may be ineffective for the discrimination of actual attention peaks [13], because of strong perceptual noise and variant stimulus types. Attention combination simulates the mechanism of attention perception in human minds, which fuses stimuli from vision, auditory and text understanding to create a unified attention. The post-attentive system justifies conclusions got in the prior steps by domain knowledge. In our mind, an attention-based system should answer the following research questions: (1) how to identify a set of effective salient features in a given sports video; (2) how to combine noisy salient features robustly; (3) how to estimate an unified attention to reflect interesting contents; and (4) how to analyse the unified attention to allocate highlights.

To avoid noise incurred by signal interpolation [12], we regard every layer in the MAR tree as an individual Markov process and limit the scope of recursive smoothing. General Highlight Detection in Sport Videos 35 Fine-to-Coarse Prediction estimates x ˆ(s|sai ) and error covariance matrix P (s|sai ) of the parent s from its children sai . 2 F (s) = Px (sr)AT (s)Px−1 (s) (19) U (s) = Px (sr) − F (s)A(s)Px (sr) (20) Coarse-to-Fine Smoothing When the fine-to-coarse filtering reaches a predefined coarse resolution or the root, the MAR has experienced all possible reflection delays and completed parameter estimation.

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