Advances in Multimedia Modeling: 18th International by Alan Hanjalic (auth.), Klaus Schoeffmann, Bernard Merialdo, 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 court cases of the 18th foreign Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised commonplace papers, 12 precise 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 purposes, seek, summarization and visualization, visualization and complicated multimedia structures, and the precise periods: interactive and immersive leisure and conversation, multimedia upkeep: find out how to determine multimedia entry over 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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Extra info for Advances in Multimedia Modeling: 18th International Conference, MMM 2012, Klagenfurt, Austria, January 4-6, 2012. Proceedings

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JMLR (2003) 3. : Evaluation of localized semantics: data, methodology, and experiments. IJCV (2007) 28 D. Kuettel, M. Guillaumin, and V. Ferrari 4. : Learning the semantics of words and pictures. In: ICCV (2001) 5. : Modeling annotated data. In: Proceedings of the ACM SIGIR Conference (2003) 6. : Exploiting hierarchical context on a large database of object categories. In: CVPR (2010) 7. : Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. , Johansen, P.

Related works. Our work relates to the numerous segment-level and image-level methods discussed above, as we seek to combine the two strands. g. by modeling co-occurence of labels [6] or their spatial relationships [23]. However, these methods typically do not use global image predictions. Most importantly, their training scenarios are radically different from ours, where ground-truth segment labels are available at training time. Therefore, they address a different task, known as semantic segmentation in the literature [14,20], which can be seen as the fully supervised version of segment-level annotation.

In: ICCV (2009) 12. : Learning a distance metric from multi-instance multi-label data. In: CVPR (2009) 13. : Image annotation via graph learning. Pattern Recognition 42(2), 218–228 (2009) 14. : Energy Minimization under Constraints on Label Counts. , Paragios, N. ) ECCV 2010. LNCS, vol. 6312, pp. 535–551. Springer, Heidelberg (2010) 15. : Nonparametric scene parsing: Label transfer via dense scene alignment. In: CVPR (2009) 16. : Label to region by bi-layer sparsity priors. In: ACM Multimedia (2009) 17.

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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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