LF-EME: Local features with elastic manifold embedding for human action recognition

Human action recognition has been an active topic in computer vision. Currently, most of the approaches to this problem can be categorized into two classes. One is based on local features, and the other is based on global features. Meanwhile, manifold learning has become successful in many problems in computer vision, but because of the high variability of human body, the application of manifold learning to human action recognition is limited. We propose a framework based on Elastic Manifold Embedding (EME), a new sparse manifold learning algorithm, together with local interest point features to handle human action recognition. The result of the new framework is very promising in comparison with state-of-the-art methods.

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

[2]  Zhigang Luo,et al.  Manifold Regularized Discriminative Nonnegative Matrix Factorization With Fast Gradient Descent , 2011, IEEE Transactions on Image Processing.

[3]  Kaizhu Huang,et al.  m-SNE: Multiview Stochastic Neighbor Embedding , 2011, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Dacheng Tao,et al.  Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[6]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[8]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[9]  Jun Yu,et al.  Complex Object Correspondence Construction in Two-Dimensional Animation , 2011, IEEE Transactions on Image Processing.

[10]  Zhigang Luo,et al.  NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization , 2012, IEEE Transactions on Signal Processing.

[11]  Lior Wolf,et al.  Local Trinary Patterns for human action recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Yang Wang,et al.  Human Action Recognition by Semilatent Topic Models , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  张振跃,et al.  Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment , 2004 .

[14]  Xindong Wu,et al.  Manifold elastic net: a unified framework for sparse dimension reduction , 2010, Data Mining and Knowledge Discovery.

[15]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[16]  Xuelong Li,et al.  General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Bo Geng,et al.  DAML: Domain Adaptation Metric Learning , 2011, IEEE Transactions on Image Processing.

[18]  Chris H. Q. Ding,et al.  Robust tensor factorization using R1 norm , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

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

[20]  Tae-Kyun Kim,et al.  Tensor Canonical Correlation Analysis for Action Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Zhigang Luo,et al.  Non-Negative Patch Alignment Framework , 2011, IEEE Transactions on Neural Networks.

[22]  Mubarak Shah,et al.  Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Xuelong Li,et al.  Geometric Mean for Subspace Selection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Ivan Laptev,et al.  On Space-Time Interest Points , 2005, International Journal of Computer Vision.

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

[26]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[27]  Shaogang Gong,et al.  Recognising action as clouds of space-time interest points , 2009, CVPR.

[28]  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.

[29]  Thomas Serre,et al.  A Biologically Inspired System for Action Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[30]  Dacheng Tao,et al.  Subspaces Indexing Model on Grassmann Manifold for Image Search , 2011, IEEE Transactions on Image Processing.

[31]  Pierre Kornprobst,et al.  Action Recognition Using a Bio-Inspired Feedforward Spiking Network , 2009, International Journal of Computer Vision.

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

[33]  Yang Jing.  L1 Regularization Path Algorithm for Generalized Linear Models , 2008 .

[34]  Greg Mori,et al.  IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL., NO. 1 Human Action Recognition by Semi-Latent Topic Models , 2022 .

[35]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories , 2006 .

[36]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[37]  Shaogang Gong,et al.  Discriminative Topics Modelling for Action Feature Selection and Recognition , 2010, BMVC.

[38]  Xuelong Li,et al.  Supervised Tensor Learning , 2005, ICDM.

[39]  Krystian Mikolajczyk,et al.  Feature Tracking and Motion Compensation for Action Recognition , 2008, BMVC.

[40]  Sergio A. Velastin,et al.  Recognizing Human Actions Using Silhouette-based HMM , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

[41]  Rémi Ronfard,et al.  Free viewpoint action recognition using motion history volumes , 2006, Comput. Vis. Image Underst..

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

[43]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[44]  Yongdong Zhang,et al.  Multiview Spectral Embedding , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[45]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[46]  Xian-Sheng Hua,et al.  Ensemble Manifold Regularization , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  Roberto Cipolla,et al.  Extracting Spatiotemporal Interest Points using Global Information , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[48]  Tianzhu Zhang,et al.  Boosted Exemplar Learning for human action recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[49]  Zhigang Luo,et al.  Online Nonnegative Matrix Factorization With Robust Stochastic Approximation , 2012, IEEE Transactions on Neural Networks and Learning Systems.

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

[51]  H. Hotelling.  Analysis of a complex of statistical variables into principal components. , 1933 .

[52]  Hossein Ragheb,et al.  MuHAVi: A Multicamera Human Action Video Dataset for the Evaluation of Action Recognition Methods , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[53]  Shuicheng Yan,et al.  Neighborhood preserving embedding , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[54]  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.

[55]  Yihong Gong,et al.  Human action detection by boosting efficient motion features , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[56]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[57]  Meng Wang,et al.  Adaptive Hypergraph Learning and its Application in Image Classification , 2012, IEEE Transactions on Image Processing.

[58]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.