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- W4385882008 abstract "Video data can often contain plenty of information that would be useful to analyze. However, most methods of collecting video data involve setting a camera to record for long periods of time, gathering mostly uninteresting data interspersed by sections of interesting events. Unlike past work in video cleaning which mostly deals with fixing incorrect labels, this paper explore ways to pick out the sections of interest from a mostly unlabeled video and only using small amounts of labeled data for training. Using only standard video features for generality, a variation of k-nearest neighbors is proposed and compared to other simple machine learning classifiers. These techniques are shown to be able to achieve up to 94% accuracy on the test data with as few as 100 training samples." @default.
- W4385882008 created "2023-08-17" @default.
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- W4385882008 date "2023-06-14" @default.
- W4385882008 modified "2023-10-16" @default.
- W4385882008 title "Machine Learning Classification for Video Cleaning" @default.
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- W4385882008 doi "https://doi.org/10.1109/bmsb58369.2023.10211506" @default.
- W4385882008 hasPublicationYear "2023" @default.
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