Prototype Learning Using Metric Learning Based Behavior Recognition

Behavior recognition is an attractive direction in the computer vision domain. In this paper, we propose a novel behavior recognition method based on prototype learning using metric learning. Prototype learning algorithm can improve the classification performance of nearest-neighbor classifier, reduce the storage and computation requirements. And the metric learning algorithm is used to advance the performance of the prototype learning. In this paper, We use a kind of compound feature including local feature and motion feature to recognize human behaviors. The experimental results show the effectiveness of our method.

[1]  Biing-Hwang Juang,et al.  Discriminative learning for minimum error classification [pattern recognition] , 1992, IEEE Trans. Signal Process..

[2]  R. Venkatesh Babu,et al.  Recognition of human actions using motion history information extracted from the compressed video , 2004, Image Vis. Comput..

[3]  Masaki Nakagawa,et al.  Evaluation of prototype learning algorithms for nearest-neighbor classifier in application to handwritten character recognition , 2001, Pattern Recognit..

[4]  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.

[5]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Ivan Laptev,et al.  On Space-Time Interest Points , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

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

[8]  Stephen J. Maybank,et al.  Human Action Recognition under Log-Euclidean Riemannian Metric , 2009, ACCV.

[9]  Atsushi Sato,et al.  Generalized Learning Vector Quantization , 1995, NIPS.

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

[11]  Atsushi Sato,et al.  A formulation of learning vector quantization using a new misclassification measure , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

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

[13]  Martial Hebert,et al.  Efficient visual event detection using volumetric features , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[14]  Klaus Obermayer,et al.  Soft Learning Vector Quantization , 2003, Neural Computation.

[15]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[16]  Xiaobo Jin,et al.  Prototype learning with margin-based conditional log-likelihood loss , 2008, 2008 19th International Conference on Pattern Recognition.