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

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

This ebook constitutes the lawsuits of the eleventh Pacific Rim convention on Advances in Multimedia info Processing, held in Shanghai in September 2010.

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Extra info for Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings

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Combination weight optimization - Optimal weight calculation to optimize search performance. It is easy to find that we can generate a model via training to predict the optimal combination strategies from certain features of each query. In this case, the second step - query matching could be redundant. In addition, developing intelligent schemes for online fusion process is becoming an important issue for many real applications. Unfortunately, Large Scale Rich Media Information Search 17 no existing study examines how to improve robustness of combination scheme for the purpose of accommodating a large number of diverse user queries simultaneously.

In this paper, we use the BBR software to get the estimate of coefficients (β and r) for sparse logistic regression defined in Equation (2). edu/~madigan/BBR/). 3 Sparse Logistic Regression for Correlation Learning For N training images with total P visual words and J tags, the association between a given tag and the visual words can be modeled by a logistic regression model. This model can then be used to annotate a non-labeled image. We apply sparse logistic regression to learn the association between visual words X and tags Y.

The most straightforward approach to performing multi-label annotation is to construct a G. Qiu et al. ): 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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