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In this paper, we propose a new algorithm to find video clips with different temporal durations and some spatial variations. We adopt a longest common sub-sequence (LCS) matching technique for measuring the temporal similarity between video clips. Based on the measure we propose 3 techniques to improve the retrieval effectiveness. First, we use a few coefficients in the low frequency region of DCT block as the basis to represent spatial features. Second, we heuristically determine a suitable quantization step-size for visual features to better tolerate spatial variations of similar video clips and propose a paired quantizer method. Third, we incorporate the compactness and/or continuity of matched common sub-sequences in the LCS measure to better reflect temporal characteristics of video. The performance of the proposed algorithm shows an improvement of 63.5% in terms of MAP (mean average precision) as compared to an existing algorithm. The results show that our approach is effective for news video retrieval.
Kim et al. (Fri,) studied this question.
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