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Tele-immersive systems, are growing in popularity and sophistication. They generate 3D video content in large scale, yielding challenges for executing data-mining tasks. Some of the tasks include classification of actions, recognizing and learning actor movements and so on. Fundamentally, these tasks require tagging and identifying of the features present in the tele-immersive 3D videos. The authors target the problem of 3D feature extraction, a relatively unexplored direction. In this paper they propose Samera, a scalable and memory-efficient feature extraction algorithm which works on short 3D video segments.
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