By Qiong Liu, You Yang, Xu Wang, Liujuan Cao (auth.), Shipeng Li, Abdulmotaleb El Saddik, Meng Wang, Tao Mei, Nicu Sebe, Shuicheng Yan, Richang Hong, Cathal Gurrin (eds.)
The two-volume set LNCS 7732 and 7733 constitutes the completely refereed complaints of the nineteenth foreign convention on Multimedia Modeling, MMM 2012, held in Huangshan, China, in January 2013.
The 30 revised common papers, forty six targeted consultation papers, 20 poster consultation papers, and 15 demo consultation papers, and six video browser showdown have been conscientiously reviewed and chosen from numeroues submissions. the 2 volumes comprise papers provided within the topical sections on multimedia annotation I and II, interactive and cellular multimedia, class, popularity and monitoring I and II, score in seek, multimedia illustration, multimedia structures, poster papers, specific consultation papers, demo consultation papers, and video browser showdown.
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Extra info for Advances in Multimedia Modeling: 19th International Conference, MMM 2013, Huangshan, China, January 7-9, 2013, Proceedings, Part II
The relevant technologies have also been investigated in video driven facial animation, and mesh coding in MPEG-4, which is mostly a choice of compression parameters and the tracking method is not standardized. Currently, MBC-Face has following progresses. For facial motion tracking, feature-based approaches usually suﬀer from drifting. To overcome it, some measures should be utilized. Appearance-based approaches are generally more robust than the above, and can be performed deterministically or statistically.
Therefore, text recognition has the widely applied prospect, and it is critical in content-based image retrieval, assistive navigation, scene understanding and geography annotation, etc. Although, the optical character recognition (OCR) has been successful in the application, it is suitable to deal with the scanned document in which the text is not interfered by the background and is easy to be distinguished from the background. Thus, text recognition in the wild is still a challenging problem.
These sentiments can be used to describe the favorability of the images. The problem of comments based favorability measurement is how to convert the natural words based comments into a numerical parameter describing the degree of favorite for an image. There is a huge gap between natural words and the numerical parameters. Fortunately, these comments written by natural words can be processed by sentiment analysis or opinion mining methods. Sentiment analysis or opinion mining refers to the application of natural language processing, computational linguistics, and text analytics to identify and extract subjective information in source materials .