Kernel-Based on Data Fusion for Image Classification with Body Energy Action Model - Volume 1, No. 2, December 2013 - International Journal of Signal Processing Systems (IJSPS)
1. How to submit my research paper? What’s the process of publication of my paper?
The journal receives submitted manuscripts via email only. Please submit your research paper in .doc or .pdf format to the submission email: ijsps@ejournal.net.
2. Can I submit an abstract?
The journal publishes full research papers. So only full paper submission should be considered for possible publication...[Read More]

Kernel-Based on Data Fusion for Image Classification with Body Energy Action Model

Nutchanun Chinpanthana 1 and Tejtasin Phiasai 2
1. Faculty of Information Technology, Dhurakij Pundit University, 110/1-4 Prachachuen Rd., Laksi, Bangkok 10210, Thailand
2. Faculty of Engineering, King Mongkut's University of Technology Thonburi, Pracha-utid Rd., Bangmod, Bangkok 10140, Thailand
Abstract—Human action classification has been and still a highly interesting and important research topic. The research results are capable of analyzing human action that we visually perceive in many aspects. Therefore the research requires an effective and competent approach to accurately interpret human action. In this paper, we present a novel model called the body energy action model for finding actual semantic action. The model is based on the fundamental concepts of biomechanics that human movement in different classes is likely to spend different amounts of energy. The model is classified by using kernel-base data fusion to obtain from the 5-fold cross validation. Experimental results show that the proposed provides much more authentic meaning of human actions.

Index Terms—human action, classification, semantic images, image classification, kernel function

Cite: Nutchanun Chinpanthana and Tejtasin Phiasai, "Kernel-Based on Data Fusion for Image Classification with Body Energy Action Model," International Journal of Signal Processing Systems, Vol. 1, No. 2, pp. 218-224, December 2013. doi: 10.12720/ijsps.1.2.218-224

Copyright © 2012-2015 International Journal of Signal Processing Systems, All Rights Reserved