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• W2003543060 abstract "Recent work shows how to use local spatio-temporal features to learn models of realistic human actions from video. However, existing methods typically rely on a predefined spatial binning of the local descriptors to impose spatial information beyond a pure “bag-of-words” model, and thus may fail to capture the most informative space-time relationships. We propose to learn the shapes of space-time feature neighborhoods that are most discriminative for a given action category. Given a set of training videos, our method first extracts local motion and appearance features, quantizes them to a visual vocabulary, and then forms candidate neighborhoods consisting of the words associated with nearby points and their orientation with respect to the central interest point. Rather than dictate a particular scaling of the spatial and temporal dimensions to determine which points are near, we show how to learn the class-specific distance functions that form the most informative configurations. Descriptors for these variable-sized neighborhoods are then recursively mapped to higher-level vocabularies, producing a hierarchy of space-time configurations at successively broader scales. Our approach yields state-of-the-art performance on the UCF Sports and KTH datasets." @default.
• W2003543060 created "2016-06-24" @default.
• W2003543060 creator A5012765543 @default.
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• W2003543060 date "2010-06-01" @default.
• W2003543060 modified "2023-10-03" @default.
• W2003543060 title "Learning a hierarchy of discriminative space-time neighborhood features for human action recognition" @default.
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• W2003543060 doi "https://doi.org/10.1109/cvpr.2010.5539881" @default.
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