Novel Kernel-Based Recognizers of Human Actions

We study unsupervised and supervised recognition of human actions in video sequences. The videos are represented by probability distributions and then meaningfully compared in a probabilistic framework. We introduce two novel approaches outperforming state-of-the-art algorithms when tested on the KTH and Weizmann public datasets: an unsupervised nonparametric kernel-based method exploiting the Maximum Mean Discrepancy test statistic; and a supervised method based on Support Vector Machine with a characteristic kernel specifically tailored to histogram-based information.

[1]  Ayhan Demiriz,et al.  Linear Programming Boosting via Column Generation , 2002, Machine Learning.

[2]  Francesca Odone,et al.  Building kernels from binary strings for image matching , 2005, IEEE Transactions on Image Processing.

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

[4]  T. Poggio,et al.  Cognitive neuroscience: Neural mechanisms for the recognition of biological movements , 2003, Nature Reviews Neuroscience.

[5]  Sebastian Nowozin,et al.  Discriminative Subsequence Mining for Action Classification , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  Nozha Boujemaa,et al.  Generalized histogram intersection kernel for image recognition , 2005, IEEE International Conference on Image Processing 2005.

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

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

[9]  Tomaso Poggio,et al.  Models of object recognition , 2000, Nature Neuroscience.

[10]  Jianbo Shi,et al.  Detecting unusual activity in video , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[11]  Ronen Basri,et al.  Actions as space-time shapes , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[13]  Bernhard Schölkopf,et al.  Injective Hilbert Space Embeddings of Probability Measures , 2008, COLT.

[14]  Juan Carlos Niebles,et al.  Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words , 2006, BMVC.

[15]  Michael J. Black,et al.  The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..

[16]  Andrew Gilbert,et al.  Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners , 2008, ECCV.

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

[18]  Zaïd Harchaoui,et al.  Testing for Homogeneity with Kernel Fisher Discriminant Analysis , 2007, NIPS.

[19]  Hans-Peter Kriegel,et al.  Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.

[20]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[21]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

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

[23]  Antonino Casile,et al.  Critical features for the recognition of biological motion. , 2005, Journal of vision.

[24]  Michael J. Swain,et al.  Color indexing , 1991, International Journal of Computer Vision.

[25]  Bernhard Schölkopf,et al.  Characteristic Kernels on Groups and Semigroups , 2008, NIPS.

[26]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[27]  Somayeh Danafar,et al.  Action Recognition for Surveillance Applications Using Optic Flow and SVM , 2007, ACCV.

[28]  Shaogang Gong,et al.  Video behaviour profiling and abnormality detection without manual labelling , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[29]  Jianbo Shi,et al.  Detecting unusual activity in video , 2004, CVPR 2004.

[30]  Eli Shechtman,et al.  Space-time behavior based correlation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[31]  J. Sullivan,et al.  Action Recognition by Shape Matching to Key Frames , 2002 .

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

[33]  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).

[34]  Liang Wang,et al.  Recognizing Human Activities from Silhouettes: Motion Subspace and Factorial Discriminative Graphical Model , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[35]  Luc Van Gool,et al.  Action snippets: How many frames does human action recognition require? , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Ivan Laptev,et al.  Local Descriptors for Spatio-temporal Recognition , 2004, SCVMA.

[37]  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.

[38]  Bernhard Schölkopf,et al.  A Kernel Method for the Two-Sample-Problem , 2006, NIPS.

[39]  Michal Irani,et al.  Detecting Irregularities in Images and in Video , 2005, ICCV.

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

[41]  Jesse Hoey,et al.  Hierarchical unsupervised learning of facial expression categories , 2001, Proceedings IEEE Workshop on Detection and Recognition of Events in Video.

[42]  Martial Hebert,et al.  Spatio-temporal Shape and Flow Correlation for Action Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Jianyong Wang,et al.  Mining sequential patterns by pattern-growth: the PrefixSpan approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[44]  Yang Wang,et al.  Unsupervised Discovery of Action Classes , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[45]  Bernhard Schölkopf,et al.  Kernel Measures of Conditional Dependence , 2007, NIPS.

[46]  Michael I. Jordan,et al.  Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces , 2004, J. Mach. Learn. Res..

[47]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.