Abstract
For the objective measurement of animal behavior from video, automated recognition systems are frequently employed. These systems rely on action models learned from labeled example videos. Manually labeling videos of animal behavior however is time consuming and error-prone. We propose to reduce the labeling effort by selecting suitable training instances
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