Efficient similarity search by summarization in large video database
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To manipulate a large video database, effective video indexing and retrieval are required. A large number of video retrieval algorithms have been presented for framewise user query or video content query, whereas few video-sequence matching algorithms have been investigated. In this paper, we propose an efficient algorithm for video sequence matching using the modified Hausdorff distance and the directed divergence of histograms between successive frames. To effectively match the video sequences with a low computational load, we use the key frames extracted by the cumulative directed divergence and compare the set of key frames using the modified Hausdorff distance. Experimental results with color video sequences show that the proposed algorithms for video sequence matching yield better performance than conventional algorithms such as histogram difference, histogram intersection, and chi-square test methods