Title :
Stacked Denoising Autoencoder for feature representation learning in pose-based action recognition
Author :
Budiman, A. ; Fanany, M.I. ; Basaruddin, C.
Author_Institution :
Fac. of Comput. Sci., Univ. of Indonesia, Depok, Indonesia
Abstract :
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.
Keywords :
feature extraction; image classification; image denoising; image motion analysis; learning (artificial intelligence); multilayer perceptrons; pattern clustering; pose estimation; ELM supervised classifier; Italian body language actions; SDA model; epoch classifier; feature representation learning; human pose-based action recognition; iterative learning; k-means clustering; marginalized SDA; multilayer perceptron; neural network supervised classifier; pose clustering; stacked denoising autoencoder; Accuracy; Joints; Neural networks; Noise reduction; Training; Tuning;
Conference_Titel :
Consumer Electronics (GCCE), 2014 IEEE 3rd Global Conference on
Conference_Location :
Tokyo
DOI :
10.1109/GCCE.2014.7031302
