Download 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.)

This publication constitutes the refereed lawsuits of the 18th foreign Multimedia Modeling convention, MMM 2012, held in Klagenfurt, Austria, in January 2012. The 38 revised general papers, 12 specific consultation papers, 15 poster consultation papers, and six demo consultation papers have been rigorously reviewed and chosen from 142 submissions. The papers are equipped 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 complex multimedia structures, and the precise classes: interactive and immersive leisure and communique, multimedia upkeep: the right way to ascertain multimedia entry through the years, multi-modal and cross-modal seek, and video surveillance.

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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. : A New Baseline for Image Annotation. , Zisserman, A. ) ECCV 2008, Part III.

Therefore, the num mber of nodes added from one layer to the upper onee is fixed to three. We define fo our layers, the bottom one containing only the seed, and the top one containing a grap ph built upon the seed and its 9 nearest neighbors, see examples in Figure 2. 3 Graph Compariison In order to integrate these new n graph features in a Bag-of-Visual-Words frameworrk a dissimilarity measure and a clustering method have to be defined. In this section, we define the dissimilarity meaasure. We are dealing with attributed graphs, where noodes can be compared with respeect to their visual appearance.

3). See [11] for details. 20 D. Kuettel, M. Guillaumin, and V. Ferrari 4 Segment Label Estimation (B) We discuss here models to estimate segment labels from image labels during training (fig. 1(b), arrow B). This stage is necessary since only ground-truth image labels are available for training. Estimating segment labels from image labels can be seen under different points of view: as a Multiple Instance Learning problem [12] where an image forms a bag of instances (segments); as a constrained clustering problem [7]; or the missing segment labels can be recovered by MRFs [21].

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