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Learning a Hierarchy of Discriminative Space-Time Neighborhood Features for Human Action Recognition

10 years 9 months ago
Learning a Hierarchy of Discriminative Space-Time Neighborhood Features for Human Action Recognition
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 configura...
Adriana Kovashka, Kristen Grauman
Added 01 Apr 2010
Updated 14 May 2010
Type Conference
Year 2010
Where CVPR
Authors Adriana Kovashka, Kristen Grauman
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