| Abstract |
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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.
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 Additional Information
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Index Terms- video retrieval, sequence matching, longest common sub-sequence, similarity measure
Citation:
Young-tae Kim, Tat-Seng Chua,
"Retrieval of News Video Using Video Sequence Matching,"
mmm,
pp. 68-75,
11th International Multimedia Modelling Conference (MMM'05),
2005
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