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 complaints of the nineteenth overseas convention on Multimedia Modeling, MMM 2012, held in Huangshan, China, in January 2013.
The 30 revised average papers, forty six distinct 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 include papers provided within the topical sections on multimedia annotation I and II, interactive and cellular multimedia, type, popularity and monitoring I and II, rating in seek, multimedia illustration, multimedia platforms, poster papers, exact 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
N M n(di , xj ) log P (di , xj ) L= (2) i=1 j=1 where n(di , xj ) is the count of element xj in document di . The steps of the EM algorithm can be succinctly described as follows. E-step. The conditional distribution P (zk |di , xj ) is computed from the previous estimate of the parameters: P (zk |di , xj ) = P (zk |di )P (xj |zk ) K l=1 P (zl |di )P (xj |zl ) (3) M-step. The parameters P (xj |zk ) and P (zk |di ) are updated with the new expected values P (zk |di , xj ): P (xj |zk ) = N i=1 n(di , xj )P (zk |di , xj ) M N m=1 i=1 n(di , xm )P (zk |di , xm ) (4) 16 D.
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 transductive version of our approach was evaluated on a wide range of synthetic distributions, and was also compared against two state-of-the-art learning approaches on the MIR-Flickr dataset, giving superior or comparable results. The two incremental implementations were compared on synthetic data, with SA method yielding superior performance.
Submanifold Analysis (SA): A more accurate technique for estimating the graph structure feature vector of item n + 1 relies on the analysis of the graph Semi-supervised Concept Detection by Learning the Structure 5 submanifold around it . Initially, the (k + 1) × (k + 1) similarity matrix WS is constructed between the new item and the k most similar items. Then, the sub-diagonal and sub-Laplacian matrices are derived as follows: DS (i, i) = WS (j, i), LS = DS − WS j We compute the eigenvalues 0 = λ0S ≤ λ1S ≤ ...