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 booklet constitutes the complaints 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 conscientiously reviewed and chosen from 106 submissions for the most convention and are observed through 23 displays of four precise periods. The papers are prepared in topical sections on multimedia content material research, photo and video processing, video coding and multimedia details processing, image/video processing and research, video coding and multimedia method, complicated photo and video coding, pass media studying with structural priors, in addition to effective multimedia research and utilization.
Read 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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Extra info for Advances in Multimedia Information Processing – PCM 2012: 13th Pacific-Rim Conference on Multimedia, Singapore, December 4-6, 2012. Proceedings
Based on the mean length of all videos in the development dataset, we choose the segment length of 115 seconds for our segment-based experiments. 2 Evaluation Method Evaluation of Motion Feature: Figure 2 shows our evaluation framework for motion features. We do experiment for both the adaptive keyframe-based and the proposed segment-based. To extract dense trajectory feature, we use the library published online by the author5 . To save computing time, the source code is customized to make it resample points for tracking after every 15 frames.
Other parameters are set to default. Due to the large number of features produced by dense sampling strategy, we employ the ”bag-of-words” approach to generate features for keyframes/segments. At ﬁrst, we randomly select 1,000,000 keypoints for clustering to form a codebook of 1000 visual codewords. After that, the frequency histogram of visual words is computed over keyframes/segments to generate the ﬁnal feature vector. To improve the performance of the ”bag-ofwords” approach, we also adopt the soft assignment weighting scheme which was initially proposed by Jiang in .
It apparently relates to hash quality and its time complexity is linear. Second, Hash lookup indicates that a lookup table is built by utilizing the hashing codewords, and it stresses search speed. Hash lookup often fails owing to sparse Hamming space, and as a consequence the strategy is that neighbors with Hamming radius r of the query are returned and Hash lookup time complexity is constant time. For each dataset, we simply adopt least-squares with L2 regularization for the fast prediction process, and do not tune any parameters to an optimal value, such as η, σ and λ in Algo.
Advances in Multimedia Information Processing – PCM 2012: 13th Pacific-Rim Conference on Multimedia, Singapore, December 4-6, 2012. Proceedings 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.)