Stacked Denoising Autoencoder for feature representation learning in pose-based action recognition

In this paper, we studied Stacked Denoising Autoencoder(SDA) model for Human pose-based action recognition. We used public dataset Chalearn 2013 which contains Italian body language actions from 27 persons. We studied two model of SDA for pose clustering: 1) Traditional SDA with epoch and Neural Network supervised classifier and 2) Marginalized SDA which faster and ELM supervised classifier. We used supervised classifier by using initial cluster data from K-means. We deployed global tuning that updating the weight during iterative learning.

[1]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[2]  Luc Van Gool,et al.  Does Human Action Recognition Benefit from Pose Estimation? , 2011, BMVC.

[3]  Sergio Escalera,et al.  Multi-modal gesture recognition challenge 2013: dataset and results , 2013, ICMI '13.

[4]  Christian Wolf,et al.  Sequential Deep Learning for Human Action Recognition , 2011, HBU.

[5]  Mohamad Ivan Fanany,et al.  Pose-based 3D human motion analysis using Extreme Learning Machine , 2013, 2013 IEEE 2nd Global Conference on Consumer Electronics (GCCE).

[6]  Darko Kirovski,et al.  Real-time classification of dance gestures from skeleton animation , 2011, SCA '11.

[7]  Philip S. Yu,et al.  Top 10 algorithms in data mining , 2007, Knowledge and Information Systems.

[8]  Giulio Paci,et al.  A Multi-scale Approach to Gesture Detection and Recognition , 2013, 2013 IEEE International Conference on Computer Vision Workshops.

[9]  Kilian Q. Weinberger,et al.  Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.

[10]  Mohamad Ivan Fanany,et al.  Constructive, robust and adaptive OS-ELM in human action recognition , 2014, 2014 International Conference on Industrial Automation, Information and Communications Technology.

[11]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[12]  Anil K. Jain.  Data clustering: 50 years beyond K-means , 2008, Pattern Recognit. Lett..

[13]  Honglak Lee,et al.  An Analysis of Single-Layer Networks in Unsupervised Feature Learning , 2011, AISTATS.

[14]  Yoshua Bengio,et al.  Scaling learning algorithms towards AI , 2007 .

[15]  Yonggwan Won,et al.  A Robust Online Sequential Extreme Learning Machine , 2007, ISNN.

[16]  Rasmus Berg Palm,et al.  Prediction as a candidate for learning deep hierarchical models of data , 2012 .

[17]  Feng Liu,et al.  Auto-encoder Based Data Clustering , 2013, CIARP.