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- W1937254975 abstract "High time complexity is a bottle-neck in video segmentation, classification, analysis, and retrieval. In This work we use a heuristic method called fast-converging sort-merge tree (FSMT) to construct automatically a hierarchy of small subsets of features that are progressively more useful for video data exploration. The method combines the virtues of a wrapper model approach for high accuracy, with those of a filter method approach for deriving the appropriate features quickly. FSMT speeds up a more fundamental method, the basic sort-merge tree (BSMT) approach, while retaining its performance. We demonstrate FSMT's high accuracy: it has a 0.001 error rate in a frame classification task on 75 minutes of instructional video, and a 0.98 precision and 0.89 recall in a segment retrieval task on 30 minutes of sports video. Additionally, FSMT is more than 80% faster than its predecessor, BSMT." @default.
- W1937254975 created "2016-06-24" @default.
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- W1937254975 date "2005-03-21" @default.
- W1937254975 modified "2023-09-24" @default.
- W1937254975 title "Video feature selection using fast-converging sort-merge tree" @default.
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- W1937254975 doi "https://doi.org/10.1109/icme.2004.1394676" @default.
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