Motor Side-View Recognition System Based on Wavelet Entropy and Naïve Bayesian Classifier
Yiyang Chen, Маtbеn Suchkov
Available Online June 2018.
- 10.2991/eame-18.2018.16How to use a DOI?
- artificial intelligence; wavelet entropy; naïve Bayesian classifier; cross validation
As the traffic accident becomes a serious problem, we need some more efficient methods to identify the car. Luckily, we can use artificial intelligence to recognize the motors by using side-view images. It will help a lot to solve the traffic accident. We used the wavelet entropy to extract the feature of the images. Then we employed the naïve Bayesian theory as the classifier. And we used 10-fold cross validation in our experiment. We used a three-level decomposition for WE. It got an overall accuracy of 75% in recognizing motors. In the future we will try to improve the accuracy of this method and try to identify cars form different brand.
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Yiyang Chen AU - Маtbеn Suchkov PY - 2018/06 DA - 2018/06 TI - Motor Side-View Recognition System Based on Wavelet Entropy and Naïve Bayesian Classifier BT - Proceedings of the 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018) PB - Atlantis Press SP - 78 EP - 82 SN - 2352-5401 UR - https://doi.org/10.2991/eame-18.2018.16 DO - 10.2991/eame-18.2018.16 ID - Chen2018/06 ER -