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 foreign Multimedia Modeling convention, MMM 2011, held in Taipei, Taiwan, in January 2011. The fifty one revised general papers, 25 specific consultation papers, 21 poster consultation papers, and three demo consultation papers, have been rigorously reviewed and chosen from 450 submissions. The papers are prepared in topical sections on audio, photo video processing, coding and compression; media content material shopping and retrieval; multi-camera, multi-view, and 3D platforms; multimedia indexing and mining; multimedia content material research; multimedia sign processing and communications; and multimedia purposes. The precise consultation papers care for content material research for human-centered multimedia purposes; huge scale wealthy media info administration; multimedia realizing for patron electronics; snapshot item popularity and compression; and interactive snapshot 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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Gaze-based interaction for semi-automatic photo cropping. In: SIGCHI Conference on Human Factors in Computing Systems (2006) 2. : Computer graphics with OpenGL (2003) 3. : Image retargeting using mesh parametrization. IEEE Transactions on Multimedia (2009) 4. : A visual attention model for adapting images on small displays. Multimedia Systems (2003) 5. : Automatic browsing of large pictures on mobile devices. In: ACM Multimedia (2003) 6. : Variance and bias for general loss functions. Mach. Learn.

Also, it enables parameter adjusting to favor desirable results with user preferences (shown in Fig 4). 2 Visual Retargeting Framework In this section, we come up with a general content aware retargeting framework from a supervised learning process point of view. t. boundary constraints (1) Grid-Based Retargeting with Transformation Consistency Smoothing 15 IM is the importance of pixels/regions, SP denotes the desirable pixel- or regionwise changes such as removing, shrinking, stretching and preserving.

To select a good model, a performance measurement of the mapping function should be provided to select the best model based on different types of noisy or clean data. [14] presented the prediction error to measure the effectiveness of a mapping function. A learnt function’s prediction error is related to the sum of the bias and the variance of a learning algorithm [6], which can be formulated as [14]. For the training data Q and any im , the prediction error decomposition is: EQ [(g(im; Q) − E(sp|im))2 ] =(EQ [g(im; Q)] − E[sp|im])2 + EQ [(g(im; Q) − EQ [g(im; Q)])2 ] (5) EQ [g(im; Q)] − E[sp|im])2 is bias, EQ [(g(im; Q) − EQ [g(im; Q)])2 ] is variance.

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