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Advances in Multimedia Information Processing -- PCM 2010, by Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue,

By Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue, C.-C. Jay Kuo, Michael S. Lew

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. given that its inauguration in 2000, PCM has been held in a number of 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 big annual overseas convention equipped as a discussion board for the dissemination of state of the art technological advances and study ends up in the fields of theoretical, experimental, and utilized multimedia research and processing. PCM 2010 featured a complete technical application which incorporated 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 exceptional researchers, Prof. Zhi-Hua Zhou from Nanjing collage, Dr. Yong Rui from Microsoft, and Dr. Tie-Yan Liu from Microsoft examine 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 book of the court cases, and Cheng Jin for taking care of the c- ference web 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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Additional info for Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings (Lecture Notes in Computer Science)

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Similarity functions are used to measure distance between query and the stored objects. , find all shirts in the shopping catalog similar to a chosen G. Qiu et al. ): PCM 2010, Part II, LNCS 6298, pp. 13–21, 2010. c Springer-Verlag Berlin Heidelberg 2010 14 Z. Cheng and J. , recommend other video lectures by the most popular lecturer), etc. – Media content mining: One important aspect that we are concerned with in this article is knowledge discovery techniques over rich media collections. As a subfield of data mining, its general objective is to extract implicit knowledge, data relationships, or other regular patterns [25].

We argue that the association can help boost the performance of annotation, especially when the number of training samples is small and the number of visual features is very large. Sparse logistic regression has been successfully used in bio-statistics[15] and in text data analysis [5]. This paper is another case for the successful use of sparse logistics regression. 2 Related Work Some studies have explored the use of probabilistic models to learn the joint distribution of image regions and words for image annotation [2] [17].

PCM 2010, Part II, LNCS 6298, pp. 22–30, 2010. c Springer-Verlag Berlin Heidelberg 2010 Image Annotation by Sparse Logistic Regression 23 binary classifier for each label separately using the one-against-the-rest scheme [13]. In this approach, instances relevant to each given label form the positive class, and the rest form the negative class. Since the labels of images are not independent of each other but actually often have significant correlations with each other,multi-label learning is studied recently in order to explicitly take advantage of the correlations between multiple tags during image annotation.

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