Dong Xu, Shih-Fu Chang. Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(11):1985-1997, November 2008.
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In this work, we systematically study the problem of event recognition in unconstrained news video sequences. We adopt the discriminative kernel-based method for which video clip similarity plays an important role. First, we represent a video clip as a bag of orderless descriptors extracted from all of the constituent frames and apply the earth mover’s distance (EMD) to integrate similarities among frames from two clips. Observing that a video clip is usually comprised of multiple subclips corresponding to event evolution over time, we further build a multilevel temporal pyramid. At each pyramid level, we integrate the information from different subclips with Integer-value-constrained EMD to explicitly align the subclips. By fusing the information from the different pyramid levels, we develop Temporally Aligned Pyramid Matching (TAPM) for measuring video similarity. We conduct comprehensive experiments on the TRECVID 2005 corpus, which contains more than 6,800 clips. Our experiments demonstrate that 1) the TAPM multilevel method clearly outperforms single-level EMD (SLEMD) and 2) SLEMD outperforms keyframe and multiframe-based detection methods by a large margin. In addition, we conduct in-depth investigation of various aspects of the proposed techniques such as weight selection in SLEMD, sensitivity to temporal clustering, the effect of temporal alignment, and possible approaches for speedup. Extensive analysis of the results also reveals intuitive interpretation of video event recognition through video subclip alignment at different levels
@article{Xu:2008,
Author = {Xu, Dong and Chang, Shih-Fu},
Title = {{Video Event Recognition Using Kernel Methods with Multilevel Temporal Alignment}},
Journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
Volume = {30},
Number = {11},
Pages = {1985--1997},
Month = {November},
Year = {2008}
}
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