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 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 common papers, 25 targeted 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, picture video processing, coding and compression; media content material looking 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 precise consultation papers take care of content material research for human-centered multimedia functions; huge scale wealthy media facts administration; multimedia realizing for customer electronics; snapshot item reputation 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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Our instance is committed to maintain lower variance. An optimization approach with lower variance is applied to reduce noise data’s influence. We constrain the grid-based shape transformation over rectangular grids. The change of grids aspect ratio is used to measure distortion energy in retargeting. This is advantageous than Wang’s models[18] that too many degrees of freedom often leads Grid-Based Retargeting with Transformation Consistency Smoothing 17 to deformation of objects. Moreover, we provide a user a few parameters to optimize the use of unimportant regions to keep important regions’ shape.

In total 10 students participated in user study. We showed each participant an original image and a randomly ordered sequence of retargeting results with different methods including scaling, non-homogeneous resizing[8], seam carving[9], Wang’s method[18] and our method. Each participant are required to choose the results most visually similar to the original image. As listed in Tab1, most participants prefer our results. Referring to Fig5, let’s investigate the results. For landscape, our results are comparable to Wang’s method.

In: CVPR (2010) 12. : Feature-aware texturing. In: Eurographics Symposium on Rendering (2006) 13. : Seam carving for content-aware image resizing. ACM Transactions on Graphics (2007) 14. : Neural networks and the bias/variance dilemma. Neural. Comput. (1992) 15. : Fscav: fast seam carving for size adaptation of videos. In: ACM Multimedia (2009) 16. : Learning to detect a salient object. In: CVPR (2007) 17. : Warp propagation for video resizing. In: CVPR (2010) 18. : Optimized scale-and-stretch for image resizing.

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