Advances in Multimedia Information Processing – PCM 2012: by Chao Wang, Yunhong Wang, Zhaoxiang Zhang (auth.), Weisi Lin, PDF

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.)

ISBN-10: 3642347770

ISBN-13: 9783642347771

ISBN-10: 3642347789

ISBN-13: 9783642347788

This e-book constitutes the lawsuits 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 through 23 shows of four distinct classes. The papers are equipped in topical sections on multimedia content material research, snapshot and video processing, video coding and multimedia info processing, image/video processing and research, video coding and multimedia procedure, complicated snapshot and video coding, pass media studying with structural priors, in addition to effective multimedia research and utilization.

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Finally, Section 6 concludes the paper with discussions of future work. com/t/press_statistics Multimedia Event Detection Using Segment-Based Approach 2 35 Related Work Start challenging from TRECVID 20102 , Multimedia Event Detection has drawn attention of many researchers. There are 7 teams participated in the debut challenge and 19 teams participated one year after that (TRECVID MED 2011). Many MED system have been built and different strategies have been employed for the event detection system.

The i=1 j=1 Wij κ(xj , xq ) + b, in which W ∈ IR concrete formulation is given by 26 L. S. Ip Algorithm 1. Hashing with Cauchy Graph Embedding Training 1: INPUT: A data set X = {xi ∈ IRD }n i=1 , and length of hash bits string is K. 2: OUTPUT: The hashing codewords H for data samples X . 3: Initialization: 4: H ←− Laplacian Embedding outcome as a warming start or random matrix H 5: W ←− Adjacency matrix of the k nearest neighbours graph for the given image corpus 6: Ω = {H ∈ IRK×n : He = 0} 7: the iteration number T = 0 8: repeat 9: Compute H(k+1) ←− ΠΩ (H(k) − ηt ∇Q(H(k) )), where ∇Q(H) = ∇Q(H)·i + λ (HHT − nI)H, ⎡ n in which ∇Q(H)·i = −4 · j=1 wij ( K k=1 hk (xi ) − hk (xj ) 10: T ←− T + 1 11: until convergence: Q(H(k+1) ) − Q(H(k) ) 2 F ≤ 2 ⎢ ·⎢ + σ 2 )2 ⎣ Q(H(k) ) ⎤ h1 (xi ) − h1 (xj ) ⎥ .

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.

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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.)


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