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Abstract:Shot boundary detection The shot boundary detection system in 2007 is basically the same as that of last year. We make three major modifications in the system of this year. First, CUT detector and GT detector use block based RGB color histogram with the different parameters instead of the same ones. Secondly, we add a motion detection module to the GT detector to remove the false alarms caused by camera motion or large object movements. Finally, we add a post-processing module based on SIFT feature after both CUT and GT detector. The evaluation results show that all these modifications bring performance improvements to the system. The brief introduction to each run is shown in the following table: Run_id Description Thu01 Baseline system: RGB4_48 for CUT and GT detector, no motion detector, no sift post-processing, only using development set of 2005 as training set Thu02 Same algorithm as thu01, but with RGB16_48 for CUT detector, RGB4_48 for GT detector Thu03 Same algorithm as thu02, but with SIFT post-processing for CUT Thu04 Same algorithm as thu03, but with Motion detector for GT Thu05 Same algorithm as thu04, but with SIFT post-processing for GT Thu06 Same algorithm as thu05, but no SIFT processing for CUT Thu09 Same algorithm as thu05, but with different parameters thu11 Same algorithm as thu05, but with different parameters Thu13 Same algorithm as thu05, but with different parameters Thu14 Same algorithm and parameters as thu05, but trained with all the development data from 2003-2006 High-level feature extraction We try a novel approach, Multi-Label Multi-Feature learning (MLMF learning) to learn a joint-concept distribution on the regional level as an intermediate representation. Besides, we improve our Video diver indexing system by designing new features, comparing learning algorithms and exploring novel fusion algorithms. Based on these efforts in improving feature, learning and fusion algorithms, we achieve top results in HFE this year.

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