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

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.)

ISBN-10: 3642178286

ISBN-13: 9783642178283

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 usual papers, 25 particular consultation papers, 21 poster consultation papers, and three demo consultation papers, have been conscientiously reviewed and chosen from 450 submissions. The papers are equipped in topical sections on audio, photo video processing, coding and compression; media content material shopping and retrieval; multi-camera, multi-view, and 3D structures; multimedia indexing and mining; multimedia content material research; multimedia sign processing and communications; and multimedia functions. The particular consultation papers take care of content material research for human-centered multimedia functions; huge scale wealthy media info administration; multimedia realizing for buyer electronics; picture item acceptance and compression; and interactive photograph and video search.

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Extra 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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In: CVPR (2007) 17. : Pair-activity classification by bi-trajectory analysis. In: CVPR (2008) 18. : Recognizing human group activities with localized causalities. In: CVPR (2009) 19. : Actions in Context. In: CVPR 2009 (2009) 20. : Hierarchical spatiotemporal context modeling for action recognition. In: CVPR (2009) 21. : A SIFT descriptor with global context. In: CVPR (2005) 22. : Probabilistic latent semanic indexing. In: ACM SIGIR (1999) 23. : Probability. Society for Industrial Mathematics (1992) 24.

Either user study or image similarity measurement applies to evaluate the effectiveness of a retargeting method. On the other hand, the result of content aware methods heavily relies on the quality of an importance map. Most importance maps are generated by low-level visual features such as gradient, color contrast and so on. Due to the lack of high-level features, an importance map cannot recover the meaningful object regions exactly to assign proper values to objects. As the importance map cannot truly represent a user’s attention, content aware retargeting guided by noisy importance map is actually a weakly supervised learning process.

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.

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


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