Advances in Multimedia Modeling: 17th International by Guangyu Zhu, Shuicheng Yan, Tony X. Han, Changsheng Xu

By Guangyu Zhu, Shuicheng Yan, Tony X. Han, Changsheng Xu (auth.), Kuo-Tien Lee, Wen-Hsiang Tsai, Hong-Yuan Mark Liao, Tsuhan Chen, Jun-Wei Hsieh, Chien-Cheng Tseng (eds.)

This two-volume lawsuits constitutes the refereed papers of the seventeenth foreign Multimedia Modeling convention, MMM 2011, held in Taipei, Taiwan, in January 2011. The fifty one revised standard papers, 25 targeted consultation papers, 21 poster consultation papers, and three demo consultation papers, have been rigorously reviewed and chosen from 450 submissions. The papers are equipped in topical sections on audio, photograph video processing, coding and compression; media content material searching and retrieval; multi-camera, multi-view, and 3D platforms; multimedia indexing and mining; multimedia content material research; multimedia sign processing and communications; and multimedia functions. The specific consultation papers take care of content material research for human-centered multimedia functions; huge scale wealthy media facts administration; multimedia knowing for client electronics; snapshot item popularity and compression; and interactive picture and video search.

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Additional info for Advances in Multimedia Modeling: 17th International Multimedia Modeling Conference, MMM 2011, Taipei, Taiwan, January 5-7, 2011, Proceedings, Part II

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Gij . . gN N } with its location i, j. Owing to the constraint of rectangular grids, all the grids in each row have the same height while the grids in each column have the same width. So the edge is simply denoted by E = {(x1 , y1 ), . . , (xi , yj ), (xN , yN )}, and xi , yj is the width and the height of the grid gij , respectively. Clearly, our model has fewer parameters to optimize than [18]. Computing the importance of a grid. With the uniform division of N × N grids, the importance of grid gij can be calculated as follows: sij = k,e∈g IM Sk,e Stotal × N2 (8) Stotal is the sum of importance values of all the pixels in the image.

Human action recognition by semilatent topic models. T-PAMI (2009) 5. : Unsupervised learning of human action categories using spatial-temoral words. IJCV (2008) 6. : Distincitive image features from scale-invariant keypoints. IJCV (2004) 7. : CONDENSATION - Conditional density propagation for visual tracking. IJCV (1998) 8. : Investigating causal relations by econometric models and crossspectral methods. Econometrica (1969) 9. : Recognizing realistic actions from videos in the wild. In: CVPR (2009) 10.

Lee et al. ): MMM 2011, Part II, LNCS 6524, pp. 12–24, 2011. c Springer-Verlag Berlin Heidelberg 2011 Grid-Based Retargeting with Transformation Consistency Smoothing 13 Fig. 1. Illustrating image/video retargeting from a supervised learning viewpoint Many content-aware retargeting methods have been proposed such as cropping[4,5,1], seam craving[13,9,15,11], multi-operator[10], mesh based retargeting[12,18,3,7,17]. Cropping[4,5,1] works on an attention model to detect important regions, and then crops the most important region to display.

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