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Recognizing Human Actions: A Local SVM Approach
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Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
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Improving the fisher kernel for large-scale image classification
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Human activity analysis: A review
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Action bank: A high-level representation of activity in video
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Learning latent temporal structure for complex event detection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
In defense of soft-assignment coding
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Learning spatiotemporal graphs of human activities
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Bag of visual words (BoVW) models have been widely and successfully used in video based action recognition. One key step in constructing BoVW representation is to encode feature with a codebook. Recently, a number of new encoding methods have been developed to improve the performance of BoVW based object recognition and scene classification, such as soft assignment encoding [1], sparse encoding [2], locality-constrained linear encoding [3] and Fisher kernel encoding [4]. However, their effects for action recognition are still unknown. The main objective of this paper is to evaluate and compare these new encoding methods in the context of video based action recognition. We also analyze and evaluate the combination of encoding methods with different pooling and normalization strategies. We carry out experiments on KTH dataset [5] and HMDB51 dataset [6]. The results show the new encoding methods can significantly improve the recognition accuracy compared with classical VQ. Among them, Fisher kernel encoding and sparse encoding have the best performance. By properly choosing pooling and normalization methods, we achieve the state-of-the-art performance on HMDB51.