Using a Product Manifold distance for unsupervised action recognition
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[1] Adriana Kovashka,et al. Learning a hierarchy of discriminative space-time neighborhood features for human action recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[2] S. Kollias,et al. Dense saliency-based spatiotemporal feature points for action recognition , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[3] Luc Van Gool,et al. Exploiting simple hierarchies for unsupervised human behavior analysis , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[4] Martial Hebert,et al. Efficient visual event detection using volumetric features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[5] Yui Man Lui,et al. Advances in matrix manifolds for computer vision , 2012, Image Vis. Comput..
[6] D. Wolpert. The Supervised Learning No-Free-Lunch Theorems , 2002 .
[7] Juan Carlos Niebles,et al. Unsupervised Learning of Human Action Categories , 2006 .
[8] Larry S. Davis,et al. Recognizing actions by shape-motion prototype trees , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[9] Ronald Poppe,et al. A survey on vision-based human action recognition , 2010, Image Vis. Comput..
[10] Ronen Basri,et al. Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[11] Serge J. Belongie,et al. Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.
[12] Mubarak Shah,et al. A 3-dimensional sift descriptor and its application to action recognition , 2007, ACM Multimedia.
[13] Ivan Laptev,et al. On Space-Time Interest Points , 2005, International Journal of Computer Vision.
[14] J. Ross Beveridge,et al. Action classification on product manifolds , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[15] Bruce A. Draper,et al. Unsupervised learning of micro-action exemplars using a Product Manifold , 2011, 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[16] Jake K. Aggarwal,et al. An Overview of Contest on Semantic Description of Human Activities (SDHA) 2010 , 2010, ICPR Contests.
[17] Janusz Konrad,et al. Action Recognition in Video by Sparse Representation on Covariance Manifolds of Silhouette Tunnels , 2010, ICPR Contests.
[18] Cordelia Schmid,et al. Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.
[19] Tae-Kyun Kim,et al. Tensor Canonical Correlation Analysis for Action Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[20] Barbara Caputo,et al. Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..
[21] Bruce A. Draper,et al. Unsupervised learning of human expressions, gestures, and actions , 2011, Face and Gesture 2011.
[22] Ahmed M. Elgammal,et al. Tracking People on a Torus , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Andrew Gilbert,et al. Push and Pull: Iterative grouping of media , 2011, BMVC.