Advances in Multimedia Modeling: 19th International - download pdf or read online

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.)

ISBN-10: 3642357245

ISBN-13: 9783642357244

ISBN-10: 3642357253

ISBN-13: 9783642357251

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 ordinary papers, forty six designated consultation papers, 20 poster consultation papers, and 15 demo consultation papers, and six video browser showdown have been conscientiously reviewed and chosen from numeroues submissions. the 2 volumes include papers awarded within the topical sections on multimedia annotation I and II, interactive and cellular multimedia, type, attractiveness and monitoring I and II, score in seek, multimedia illustration, multimedia platforms, poster papers, distinct consultation papers, demo consultation papers, and video browser showdown.

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Read or Download Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Huangshan, China, January 7-9, 2013, Proceedings, Part I PDF

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

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Efficient large scale image annotation by probabilistic collaborative multi-label propagation. ACM Multimedia, 35–44 (2010) 6. : Leveraging social media networks for classification. Data Mining and Knowledge Discovery 23(3), 447–478 (2011) 7. : Classification in Networked Data: A Toolkit and a Univariate Case Study. Journal of Machine Learning Research 8, 935–983 (2007) 8. : Laplacian Eigenmaps for dimensionality reduction and data representation. Neural Computing 15(6), 1373–1396 (2003) 9. : Incremental Laplacian eigenmaps by preserving adjacent information between data points.

Since the focus of this paper is not on image feature selection, we use similar features extracted by [6] to make a fair comparison with the state-of-the-art approaches. First of all, we simply decompose images into a set of 32 × 32-sized blocks, then compute a 36 dimensional feature vector for each block, consisting of 24 color features (autocorrelogram) computed over 8 quantized colors and 3 Manhattan Distances,12 texture features (Gabor filter) computed over 3 scales and 4 orientations. As a result, each block is represented as a 36-dim feature vector.

This the extended set of items and recompute the eigenvectors of L is clearly impractical in settings where new media items are regularly arriving to the indexing system. Thus, it is necessary to devise incremental means for computing the graph structure features of unknown items. Linear Projection (LP): A simple approach for deriving the graph structure features of a new item n + 1 is to determine the set Nn+1 of k most similar items and then to compute the weighted mean of their graph structure features: ˆ = Sn+1 j∈Nn+1 wn+1,j Sj j∈Nn+1 wn+1,j (4) where wn+1,j denotes the similarity score between the new item and neighbouring item j, which may be computed by use of the heat kernel (Equation 1).

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


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