Foggy Images Classification Based On Features Extraction and SVM

DOI

10.2991/icsecs-13.2013.30How to use a DOI?

Keywords

HSI model; histogram; dichromatic atmospheric scattering model; SVM

Abstract

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.

Copyright

© 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/).

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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  -