2013
CVPR
CVPR 2013
A Video Representation Using Temporal Superpixels
Abstract
We develop a generative probabilistic model for temporally consistent superpixels in video sequences. In contrast to supervoxel methods, object parts in different frames are tracked by the same temporal superpixel. We explicitly model flow between frames with a bilateral Gaussian process and use this information to propagate superpixels in an online fashion. We consider four novel metrics to quantify performance of a temporal superpixel representation and demonstrate superior performance when compared to supervoxel methods.
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Conference Pioneer
— CVPR 2013
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Vision and Machine Learning
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Keyword Pioneer
— temporal superpixel
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Hot Topic Early Bird
— temporal consistency
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio