Foggy Images Classification Based On Features Extraction and SVM
Yuanyuan Zhang, Guangmin Sun, Qian Ren, Dequn Zhao
Available Online September 2013.
- 10.2991/icsecs-13.2013.30How to use a DOI?
- HSI model; histogram; dichromatic atmospheric scattering model; SVM
An algorithm of foggy image classification is presented in this paper. First, the RGB images are converted to HSI images and next we analysis the distribution of the histograms of H, S, I plane separately, from which we extract the variance of each plane under different foggy conditions as the HSI model features. Second, the dichromatic atmospheric scattering model is introduced and based on this model we develop an algorithm for computing the angular deviation of different foggy images compared to clear day image as another feature. Finally, we use this feature set to train a multi-class SVM classifier to classify four different levels of foggy images. Experiment results show that the algorithm is more than 90% accurate.
- © 2013, 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 - Yuanyuan Zhang AU - Guangmin Sun AU - Qian Ren AU - Dequn Zhao PY - 2013/09 DA - 2013/09 TI - Foggy Images Classification Based On Features Extraction and SVM BT - Proceedings of the 2013 International Conference on Software Engineering and Computer Science PB - Atlantis Press SP - 142 EP - 145 SN - 1951-6851 UR - https://doi.org/10.2991/icsecs-13.2013.30 DO - 10.2991/icsecs-13.2013.30 ID - Zhang2013/09 ER -