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Advances in Multimedia Information Processing -- PCM 2010, - download pdf or read online

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

ISBN-10: 3642156959

ISBN-13: 9783642156953

This booklet 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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Read Online or Download Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings PDF

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Additional resources 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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In order to record individual’s life as in detail as possible, they adopted a passive capture method with SenseCam [2] to collect one’s daily experience by unobtrusively taking photos whenever the internal sensor is triggered by a change in ambient temperature, lighting or movement. It is a big challenge to put forward uniform quantitative criteria of “activity”, since it is a subject concept which originates from cognitive psychology. However, it is possible to distinguish a specific activity from others through its intrinsic attributes: person, time, location and theme.

We put all of these coefficients in a matrix W ∈ R(P +1)×J , with the jth column βˆj . Algorithm 1 gives out the discovery of association between visual words and tags by sparse logistic regression. Algorithm 1. Associations between visual words and tags Input: training data matrix X ∈ RN×P , corresponding Labels (indicator matrix) Y = (y1 , . . , yN )T ∈ {−1, 1}N×J Output: Coefficient matrix W ∈ R(P +1)×J ˜ = [1, X]T , where 1 is a column vector with all elements being 1; 1: Set X 2: for j = 1, .

Table 2 presents some examples of the annotations produced by SLR, MSC and BSVM. The results in Table 2 show that, even when SLR annotates an image with a label not contained in the ground-truth label set, this annotated label is still frequently acceptable. 5 Conclusions This paper applied sparse logistic regression on image annotation. Experiments show that this approach achieves better results than other methods. Acknowledgments This work is supported by NSFC (60833006). Jinzhu Jia is supported by the National Science Foundation, CDI grant SES-083553.

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Advances in Multimedia Information Processing -- PCM 2010, Part II: 11th Pacific Rim Conference on Multimedia, Shanghai, China, September 21-24, 2010 Proceedings by Guoping Qiu, Kin Man Lam, Hitoshi Kiya, Xiang-Yang Xue, C.-C. Jay Kuo, Michael S. Lew


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