By Chao Wang, Yunhong Wang, Zhaoxiang Zhang (auth.), Weisi Lin, Dong Xu, Anthony Ho, Jianxin Wu, Ying He, Jianfei Cai, Mohan Kankanhalli, Ming-Ting Sun (eds.)
This e-book constitutes the court cases of the thirteenth Pacific Rim convention on Multimedia, held in Singapore in the course of December 4-6, 2012. The fifty nine revised complete papers awarded have been rigorously reviewed and chosen from 106 submissions for the most convention and are followed by way of 23 displays of four specified periods. The papers are geared up in topical sections on multimedia content material research, picture and video processing, video coding and multimedia details processing, image/video processing and research, video coding and multimedia process, complex snapshot and video coding, pass media studying with structural priors, in addition to effective multimedia research and utilization.
Read Online or Download Advances in Multimedia Information Processing – PCM 2012: 13th Pacific-Rim Conference on Multimedia, Singapore, December 4-6, 2012. Proceedings PDF
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Reasoning 50(7), 969–978 (2009) 10. : Spherical hashing. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2012) 11. : Supervised hashing with kernels. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2012) 12. : Large graph construction for scalable semi-supervised learning. In: Proceedings of the 27th International Conference on Machine Learning, ICML 2010, pp. 679–686 (2010) 13. : Principles of hash-based text retrieval. In: SIGIR, pp.
To better understand th he above considerations and also explore our knowleedge about users’ responses to o quality changes a statistical analysis was employyed. Fig. 1. Results showing averaged responses of all participants with respect to variations inn the sound quality of the audiovisual clip Evaluation of Audio Quality Requirements over Extended Periods 15 Three subsets of data were created to proceed with further data examination. These subsets included: a) average quality level of the last minute of each 3 min time slot (this represents established/stationary quality preferences of the subjects) b) response time to the automatic quality degradation right after the start of each 3 min time slot c) quality level at the time when a user reacted to quality change Due to the fact that the first three minutes were designed only to make participants familiar with the top quality the results for this time section were excluded from further analysis.
K=⎣. ⎦,W = ⎣ . . 1 κ(xn , x1 ) · · · κ(xn , xs ) ws1 ˜ W 2 F, (3) ⎤ · · · w1K T .. ⎥ , and W ˜ = b . . ⎦ W · · · wsK Clearly, the ﬁrst term in (3) is a loss function that measures the similarity between the outputs of hashing functions and the hashing codes obtained in Sect. 1, and the second term W 2 is to avoid overﬁtting. λ is a trade-oﬀ parameters that can balance the loss function and the regularization term. Therefore, the optimal solution of above regularized kernel least squares (3) can be obtained ˜ = (λI + KT K)−1 KT HT .