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http://dx.doi.org/10.3837/tiis.2020.08.022

Smoke Image Recognition Method Based on the optimization of SVM parameters with Improved Fruit Fly Algorithm  

Liu, Jingwen (Central South University of Forestry and Technology)
Tan, Junshan (Central South University of Forestry and Technology)
Qin, Jiaohua (Central South University of Forestry and Technology)
Xiang, Xuyu (Central South University of Forestry and Technology)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.14, no.8, 2020 , pp. 3534-3549 More about this Journal
Abstract
The traditional method of smoke image recognition has low accuracy. For this reason, we proposed an algorithm based on the good group of IMFOA which is GMFOA to optimize the parameters of SVM. Firstly, we divide the motion region by combining the three-frame difference algorithm and the ViBe algorithm. Then, we divide it into several parts and extract the histogram of oriented gradient and volume local binary patterns of each part. Finally, we use the GMFOA to optimize the parameters of SVM and multiple kernel learning algorithms to Classify smoke images. The experimental results show that the classification ability of our method is better than other methods, and it can better adapt to the complex environmental conditions.
Keywords
smoke image; fruit fly optimization algorithm; Multiple Kernel Learning;
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