Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a new algorithm to find video clips with different temporal durations and some spatial variations.  adopt a longest common sub-sequence (LCS) matching technique for measuring the temporal similarity beten video clips. Based on the measure  propose 3 techniques to improve the retrieval effectiveness. First,  use a few coefficients in the low frequency region of DCT block as the basis to represent spatial features. Second,  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,  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.
['Young-Tae Kim', 'Tat-Seng Chua']
I am writing a research paper on a idea Retrieval of News Video Using Video Sequence Matching
[{"from": "human", "value": "I am writing a research paper on a idea Retrieval of News Video Using Video Sequence Matching"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  propose a new algorithm to find video clips with different temporal durations and some spatial variations.  adopt a longest common sub-sequence (LCS) matching technique for measuring the temporal similarity beten video clips. Based on the measure  propose 3 techniques to improve the retrieval effectiveness. First,  use a few coefficients in the low frequency region of DCT block as the basis to represent spatial features. Second,  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,  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."}]