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

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

This publication 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 provided including 2 invited talks have been rigorously reviewed and chosen from a hundred thirty five submissions. The papers are geared up in topical sections on automatic annotation, coding and streaming, video semantics and relevance, audio, acceptance, class and retrieval, in addition to question and summarization.

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Extra info for Advances in Multimedia Modeling: 15th International Multimedia Modeling Conference, MMM 2009, Sophia-Antipolis, France, January 7-9, 2009. Proceedings. ... Applications, incl. Internet Web, and HCI)

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However, SURF achieves greater computational efficiency by using integral images. In order to efficiently locate relevant images in a large database, Nist´er and Stewnius [11] propose the use of a vocabulary tree of SIFT descriptors. Our previous work on the SenseCam includes [12] and [13], where we have focused on developing SenseCam-image clustering techniques to assist user browsing by grouping the thousands of images generated by the device into events. We have also examined matching events in a person’s life [14].

We extend this general algorithm for attention combination. g. zoom depth and game pitch ratio; and gradually impose other salient features as an updated measurement in the merge step. The details of our algorithm are presented as follows. 3 sec. 1 R. M. Jose Fine-to-Coarse Filtering Let x ˆ(s|s) be the optimal estimation of attention intensity x(s) at a node s, together with P (s|s), the error covariance. Initialisation. Start with salient features at the finest temporal resolution. For each leaf s, the estimation of x ˆ(s|s−) and the covariance P (s|s−) from the sub-tree are as follows.

This provides a post-attentive explanation to question 4. In this paper, we model the perception process of sports video watching to estimate the intensity of viewer reflection. e. multi-resolution autoregressive (MAR). Adaptive selection extends the pre-attentive system, which identifies the most effective salient features to improve the robustness of attention estimation. The technique of MAR is equivalent to a Markovian process on graph [15], the general temporal model of video content presentation [16].

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