Read e-book online Advances in Multimedia Modeling: 18th International PDF

By Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, Alexander G. Hauptmann, Chong-Wah Ngo, Yiannis Andreopoulos, Christian Breiteneder (eds.)

ISBN-10: 3642273548

ISBN-13: 9783642273544

ISBN-10: 3642273556

ISBN-13: 9783642273551

This booklet constitutes the refereed complaints of the 18th overseas Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised usual papers, 12 particular consultation papers, 15 poster consultation papers, and six demo consultation papers have been rigorously reviewed and chosen from 142 submissions. The papers are prepared within the following topical sections: annotation, annotation and interactive multimedia functions, occasion and job, mining and cellular multimedia functions, seek, summarization and visualization, visualization and complex multimedia structures, and the designated classes: interactive and immersive leisure and communique, multimedia maintenance: the way to ascertain multimedia entry through the years, multi-modal and cross-modal seek, and video surveillance.

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Read Online or Download Advances in Multimedia Modeling: 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings PDF

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The proposed framework is summarized in the flowchart presented in Figure 1. Multi-layer Local Graph Words for Object Recognition 31 The paper is organized as follows, in section 2 we discuss the process of building these graphs and introduce their nested construction. In section 3, we introduce the dissimilarity measure used to compare graphs and built visual dictionaries by clustering. The latter are presented in section 4. Experiments with these new features are presented in section 5. Conclusions and perspectives are given in section 6.

3 Tagprop + Global SegProp (F) We propose a novel and more elaborate technique to predict image labels by combining image-level and segment-level information. We include both segment neighbors (as in Global Segprop) and image neighbors (as in Tagprop) Ns p(Ll (Y )|{yr }, I) = N s πyi p(Ll (si )) + i I πyi p(Ll (Ii )) (17) i Note that there are two sets of weights, π S for segment neighbors, and π I for image neighbors. By fixing one set of weights, we can maximize the log-likelihood over the other set as done for eq.

Following previous works [10,11], we measure the Break-Even Point score (BEP). To compute the BEP, first the images are ordered by the predicted probability for a label l. This list is truncated to the length of the true number of relevant images (using ground-truth). The BEP measures the percentage of relevant images in this truncated list, averaged over all labels l = 1 . . V . Some works [7,11,17] additionally measure precision/recall after assigning the 5 highest-scoring labels to each test image.

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Advances in Multimedia Modeling: 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings by Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, Alexander G. Hauptmann, Chong-Wah Ngo, Yiannis Andreopoulos, Christian Breiteneder (eds.)


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