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) was once held in Shanghai at Fudan collage, in the course of September 21–24, 2010. because its inauguration in 2000, PCM has been held in a number of areas round the Pacific Rim, particularly 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 tremendous annual foreign convention geared up as a discussion board for the dissemination of state of the art technological advances and study leads to 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 united states. 3 extraordinary researchers, Prof. Zhi-Hua Zhou from Nanjing collage, Dr. Yong Rui from Microsoft, and Dr. Tie-Yan Liu from Microsoft study Asia added 3 keynote talks to the convention. we're very thankful to the numerous those that helped to make this convention a s- cess. we want to particularly 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 site and exposure. We thank Fei Wu for organizing the certain consultation on large-scale multimedia seek within the social community settings.
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Extra info for Advances in Multimedia Information Processing - PCM 2010: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 2010, Proceedings, Part II
Jia zero coeﬃcient βpj = 0 means that the pth visual word (predictors) has zero coeﬃcient and is not predictable to the jth tag. β j is sparse due to the fact that only a few visual words are predictive to one target label. The sparse logistic regression is applied to obtain this vector. βˆj = arg min j β n T log(1 + exp(−β j x ˜i yi,j )) + P λ|βpj | (3) p=0 i=1 where x ˜i = (1, xTi )T . If |βpj |(0 ≤ p ≤ P ) is large, we can know that the pth visual word has strong association with the jth label.
He and J. Jia Table 2. 3 29 Compared Schemes In our experiments, we compare the annotation results via sparse logistic regression method (SLR) with the results via a number of related state-of-the art algorithms. We compared our results with binary SVM using one-against-the-rest scheme. In this approach, instances relevant to each given label form the positive class, and the rest form the negative class. We separately construct a binary linear SVM (BSVM) classiﬁer for each label and the trained BSVMs are used to predict labels.
Of the 14th ACM SIGIR Conference, pp. 3–12 (1991) 3. : The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research 43(3), 345–354 (2006) 4. : Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In: Proc. of the 12th International World Wide Web Conference (2003) 5. : IEvaluation campaigns and TRECVid. In: Proc. of the ACM MIR Conference (2006) 6. : Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases.
Advances in Multimedia Information Processing - PCM 2010: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 2010, Proceedings, Part II by Yang Liu, Jian Shao (auth.), Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue, C.-C. Jay Kuo, Michael S. Lew (eds.)