Research on Different Illumination Image Classification Method

DOI

10.2991/amcce-17.2017.101How to use a DOI?

Keywords

different illumination image; feature extraction; support vector machine; BP neural network; K-means

Abstract

Aiming at the problem of video image captured by the monitoring system under the conditions of haze, rain, snow and uneven illumination, a classification method of different illumination is proposed in this paper. Through analyzing the characteristics of different illumination images, the features of different illumination images can be extracted. The different illumination image features can be used to train and construct the classifier. Finally, the different illumination images are classified by the classifier. The experimental results show that the support vector machine (SVM) algorithm, BP neural network algorithm and k-means algorithm all can achieve the classification of different illumination images, and SVM algorithm has the highest classification accuracy and shortest running time.

Copyright

© 2017, 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/).

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TY  - CONF
AU  - WenLi Zhang
AU  - HongLu Li
AU  - ZhuoZheng Wang
PY  - 2017/03
DA  - 2017/03
TI  - Research on Different Illumination Image Classification Method
BT  - Proceedings of the 2017 2nd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2017)
PB  - Atlantis Press
SP  - 574
EP  - 581
SN  - 2352-5401
UR  - https://doi.org/10.2991/amcce-17.2017.101
DO  - 10.2991/amcce-17.2017.101
ID  - Zhang2017/03
ER  -