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- W2124225542 abstract "This work addresses the problem of segmenting an object of interest out of a video. We show that video object segmentation can be naturally cast as a semi-supervised learning problem and be efficiently solved using harmonic functions. We propose an incremental self-training approach by iteratively labeling the least uncertain frame and updating similarity metrics. Our self-training video segmentation produces superior results both qualitatively and quantitatively. Moreover, usage of harmonic functions naturally supports interactive segmentation. We suggest active learning methods for providing guidance to user on what to annotate in order to improve labeling efficiency. We present experimental results using a ground truth data set and a quantitative comparison to a representative object segmentation system." @default.
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- W2124225542 date "2011-01-01" @default.
- W2124225542 modified "2023-10-09" @default.
- W2124225542 title "Combining Self Training and Active Learning for Video Segmentation" @default.
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- W2124225542 doi "https://doi.org/10.5244/c.25.78" @default.
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