Robust relative attributes for human action recognition

[1]  Chunheng Wang,et al.  Action Recognition Using Context-Constrained Linear Coding , 2012, IEEE Signal Processing Letters.

[2]  Thomas B. Moeslund,et al.  Selective spatio-temporal interest points , 2012, Comput. Vis. Image Underst..

[3]  Thomas Serre,et al.  HMDB: A large video database for human motion recognition , 2011, 2011 International Conference on Computer Vision.

[4]  Kristen Grauman,et al.  Relative attributes , 2011, 2011 International Conference on Computer Vision.

[5]  Kristen Grauman,et al.  Sharing features between objects and their attributes , 2011, CVPR 2011.

[6]  Ying Wu,et al.  Action recognition with multiscale spatio-temporal contexts , 2011, CVPR 2011.

[7]  Silvio Savarese,et al.  Recognizing human actions by attributes , 2011, CVPR 2011.

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  Alexander C. Berg,et al.  Automatic Attribute Discovery and Characterization from Noisy Web Data , 2010, ECCV.

[10]  Yang Wang,et al.  A Discriminative Latent Model of Object Classes and Attributes , 2010, ECCV.

[11]  Stefano Soatto,et al.  Tracklet Descriptors for Action Modeling and Video Analysis , 2010, ECCV.

[12]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  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.

[14]  Cordelia Schmid,et al.  Evaluation of Local Spatio-temporal Features for Action Recognition , 2009, BMVC.

[15]  Shree K. Nayar,et al.  Attribute and simile classifiers for face verification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[16]  Andrew Gilbert,et al.  Fast realistic multi-action recognition using mined dense spatio-temporal features , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Larry S. Davis,et al.  Recognizing actions by shape-motion prototype trees , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[18]  Dong Han,et al.  Selection and context for action recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[19]  Jake K. Aggarwal,et al.  Spatio-temporal relationship match: Video structure comparison for recognition of complex human activities , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Christoph H. Lampert,et al.  Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  M. Shah,et al.  Learning semantic visual vocabularies using diffusion distance , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  C. Schmid,et al.  Actions in context , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  Jieping Ye,et al.  Multi-Task Feature Learning Via Efficient l2, 1-Norm Minimization , 2009, UAI.

[25]  Massimiliano Pontil,et al.  Convex multi-task feature learning , 2008, Machine Learning.

[26]  Du Tran,et al.  Human Activity Recognition with Metric Learning , 2008, ECCV.

[27]  Yoshua Bengio,et al.  Zero-data Learning of New Tasks , 2008, AAAI.

[28]  Mubarak Shah,et al.  Learning human actions via information maximization , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Jaime G. Carbonell,et al.  Fast learning of document ranking functions with the committee perceptron , 2008, WSDM '08.

[31]  Juan Carlos Niebles,et al.  Spatial-Temporal correlatons for unsupervised action classification , 2008, 2008 IEEE Workshop on Motion and video Computing.

[32]  R. Basri,et al.  Actions as Space-Time Shapes , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Ramakant Nevatia,et al.  Single View Human Action Recognition using Key Pose Matching and Viterbi Path Searching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[35]  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.

[36]  I. Laptev,et al.  On Space-Time Interest Points , 2005, ICCV 2005.

[37]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[38]  Mubarak Shah,et al.  Actions sketch: a novel action representation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[39]  B. Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[40]  Jitendra Malik,et al.  Recognizing action at a distance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[41]  Fernando Pérez-Cruz,et al.  Empirical risk minimization for support vector classifiers , 2003, IEEE Trans. Neural Networks.

[42]  Jake K. Aggarwal,et al.  Human motion analysis: a review , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[43]  Ivan Laptev,et al.  Improving bag-of-features action recognition with non-local cues , 2010, BMVC.