Choosing Multiple Parameters for Support Vector Machines
Machine Learning
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Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection
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ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Object, scene and actions: combining multiple features for human action recognition
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LIBSVM: A library for support vector machines
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Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Action recognition with multiscale spatio-temporal contexts
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CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Video from nearly still: An application to low frame-rate gait recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we propose the canonical correlation kernel (CCK), that seamlessly integrates the advantages of lower dimensional representation of videos with a discriminative classifier like SVM. In the process of defining the kernel, we learn a low-dimensional (linear as well as nonlinear) representation of the video data, which is originally represented as a tensor. We densely compute features at single (or two) frame level, and avoid any explicit tracking. Tensor representation provides the holistic view of the video data, which is the starting point of computing the CCK. Our kernel is defined in terms of the principal angles between the lower dimensional representations of the tensor, and captures the similarity of two videos in an efficient manner. We test our approach on four public data sets and demonstrate consistent superior results over the state of the art methods, including those that use canonical correlations.