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This two-volume complaints constitutes the refereed papers of the seventeenth overseas Multimedia Modeling convention, MMM 2011, held in Taipei, Taiwan, in January 2011. The fifty one revised general papers, 25 detailed 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 looking and retrieval; multi-camera, multi-view, and 3D structures; multimedia indexing and mining; multimedia content material research; multimedia sign processing and communications; and multimedia purposes. The designated consultation papers care for content material research for human-centered multimedia functions; huge scale wealthy media facts administration; multimedia realizing for purchaser electronics; picture item reputation and compression; and interactive photo 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
We proposed to measure retargeting performance based on prediction error decomposition . We further propose a grid-based retargeting model to ensures transformation consistency in model learning. This grid-based model is optimized by solving a nonlinear programming problem. There are two merits in the proposed framework: (1) our framework is generalized for previous works, in which by incorporating diﬀerent learning functions, many state-of-theart retargeting methods can be derived from our framework.
7 descriptors are available that distinguish irregular textured regions out from objects, our method is able to produce more desirable results. 5 Conclusion and Discussions We proposed a general content aware retargeting framework from a supervised learning viewpoint. We proposed to measure retargeting performance based on prediction error decomposition . We further propose a grid-based retargeting model to ensures transformation consistency in model learning. This grid-based model is optimized by solving a nonlinear programming problem.
Inﬂuence of the importance variant’s power on the resulting weights GSk,e and ASk,e is important value of the pixel at the position (k, e) in the gradient map and the attention map, respectively. , higher values in a gradient map, people can tolerate such distortions. Consequently, the addition of an attention map would reduce the overall importance values in irregular textured regions. 2 Grid-Based Resizing Model The grids are divided as follows. An image is divided into N × N grids, and the grids are denoted by M = (V, E) in which V is the 2D grid coordinate, E are the edges of grids.