Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
Published: Jun 1, 2011
Abstract
Previous work on action recognition has focused on adapting hand-designed local features, such as SIFT or HOG, from static images to the video domain. In this paper, we propose using unsupervised feature learning as a way to learn features directly from video data. More specifically, we present an extension of the Independent Subspace Analysis algorithm to learn invariant spatio-temporal features from unlabeled video data. We discovered that,...
Paper Details
Title
Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis
Published Date
Jun 1, 2011
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