The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
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Recognizing Human Actions: A Local SVM Approach
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Behavior recognition via sparse spatio-temporal features
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Motion Characterization Using Spatio-temporal Features
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ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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This paper presents a new action recognition approach based on local spatio-temporal features. The main contributions of our approach are twofold. First, a new local spatio-temporal feature is proposed to represent the cuboids detected in video sequences. Specifically, the descriptor utilizes the covariance matrix to capture the self-correlation information of the low-level features within each cuboid. Since covariance matrices do not lie on Euclidean space, the Log-Euclidean Riemannian metric is used for distance measure between covariance matrices. Second, the Earth Mover’s Distance (EMD) is used for matching any pair of video sequences. In contrast to the widely used Euclidean distance, EMD achieves more robust performances in matching histograms/distributions with different sizes. Experimental results on two datasets demonstrate the effectiveness of the proposed approach.