By Yang Liu, Jian Shao (auth.), Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue, C.-C. Jay Kuo, Michael S. Lew (eds.)
The 2010 Pacific-Rim convention on Multimedia (PCM 2010) used to be held in Shanghai at Fudan college, in the course of September 21–24, 2010. for the reason that its inauguration in 2000, PCM has been held in quite a few areas round the Pacific Rim, specifically Sydney (PCM 2000), Beijing (PCM 2001), Hsinchu (PCM 2002), Singapore (PCM 2003), Tokyo (PCM 2004), Jeju (PCM 2005), Zhejiang (PCM 2006), Hong Kong (PCM 2007), Tainan (PCM 2008), and Bangkok (PCM 2009). PCM is a huge annual foreign convention geared up as a discussion board for the dissemination of state of the art technological advances and learn ends up in the fields of theoretical, experimental, and utilized multimedia research and processing. PCM 2010 featured a accomplished technical software which integrated seventy five oral and fifty six poster shows chosen from 261 submissions from Australia, Canada, China, France, Germany, Hong Kong, India, Iran, Italy, Japan, Korea, Myanmar, Norway, Singapore, Taiwan, Thailand, the united kingdom, and the us. 3 extraordinary researchers, Prof. Zhi-Hua Zhou from Nanjing collage, Dr. Yong Rui from Microsoft, and Dr. Tie-Yan Liu from Microsoft learn Asia brought 3 keynote talks to the convention. we're very thankful to the various those who helped to make this convention a s- cess. we wish to in particular thank Hong Lu for neighborhood association, Qi Zhang for dealing with the e-book of the lawsuits, and Cheng Jin for taking care of the c- ference web site and exposure. We thank Fei Wu for organizing the particular consultation on large-scale multimedia seek within the social community settings.
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Additional resources for Advances in Multimedia Information Processing - PCM 2010: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 2010, Proceedings, Part II
LPP performed better when reduced into a relatively higher dimension, while the other two methods remained in a low performance. 4 Experiment Using Auto-encoder with Diﬀerent Depth Another interesting question is whether adding the depth of the auto-encoder is useful to improve the classiﬁcation accuracy. Theoretically, the deep architecture is capable of capturing more complex dependencies among the input variables than the shallow one, however, the training stage of the auto-encoder is unsupervised so when the architecture get deeper, more noise data might be involved.
This is because huge amount of labor cost is required for query topic design. ) to cover sufficient knowledge to achieve good generalization capability when dealing with unseen queries issued by users. On the other hand, for many existing QDF schemes, generic procedure for deriving combination strategy includes three basic steps: – Feature extraction - Calculating features to represent different queries. – Query matching - Matching different queries based on features generated in step 1. – Combination weight optimization - Optimal weight calculation to optimize search performance.
These approaches are somehow incompetent to help augment memory when applied to PPLs organization directly. Nowadays, when one engages in some activity, he may probably produce lots of digital records like email, calendar, SMS message and photograph. These objects, which reflect a same activity from different aspects, are usually starting points to inspire a distant memory. Nevertheless, it is difficult to get an intact memory of the whole activity merely upon little vague clues implied on single PLL object.