By Symeon Papadopoulos, Christos Sagonas, Ioannis Kompatsiaris, Athena Vakali (auth.), Shipeng Li, Abdulmotaleb El Saddik, Meng Wang, Tao Mei, Nicu Sebe, Shuicheng Yan, Richang Hong, Cathal Gurrin (eds.)

The two-volume set LNCS 7732 and 7733 constitutes the completely refereed court cases of the nineteenth foreign convention on Multimedia Modeling, MMM 2012, held in Huangshan, China, in January 2013.
The 30 revised general papers, forty six unique consultation papers, 20 poster consultation papers, and 15 demo consultation papers, and six video browser showdown have been rigorously reviewed and chosen from numeroues submissions. the 2 volumes comprise papers offered within the topical sections on multimedia annotation I and II, interactive and cellular multimedia, type, attractiveness and monitoring I and II, rating in seek, multimedia illustration, multimedia platforms, poster papers, detailed consultation papers, demo consultation papers, and video browser showdown.

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Extra info for Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Huangshan, China, January 7-9, 2013, Proceedings, Part I

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Especially those with different annotating orders and enriched 20 D. Tian, X. Zhao, and Z. Shi Table 1. 25 Mean per-word precision Fig. 3. Annotation comparison between PLSA-FUSION and PLSA-RW (Re-ranked and enriched annotations are underlined and italic) annotating keywords compared to the PLSA-FUSION and the ground truth annotation are underlined and italic, respectively. 3 Refining Image Annotation on Mirflickr25k To further demonstrate the effectiveness of PLSA-RW proposed in this paper, we also conduct experiment on Mirflickr25k dataset 2 , which contains 25000 images with 1386 labels.

5. Performance gains by adding unlabeled samples 5 Conclusions We presented GSF, a multimedia annotation approach leveraging the structure of image similarity graphs. GSF relies on the assumption that images with similar positions on the graph tend to carry the same concepts. Concept detection is then conducted by training classifiers using the graph Laplacian eigenvectors as features. Apart from the transductive formulation of the problem, we proposed two incremental versions, one based on Linear Projection and the other on Submanifold Analysis, and described four fusion techniques applicable to GSF.

The automatic tagging for the item is based on the most likely tags derived from the latent topic identified. Similar to our approach, graph-based approaches have been studied. Hotho et al. [8] proposed a variant of PageRank, called FolkRank, that performs on a tripartite graph induced by a folksonomy. FolkRank used a folksonomy structure for tag recommendations and searches requested within tagging systems. In recent work, Ramezani [13] modeled folksonomy as a weighted directed graph. They then applied the PageRank algorithm to this graph so as to enhance graph-based tag recommendation techniques.

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