• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

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

Vol. 14, No. 8, August 31, 2020
10.3837/tiis.2020.08.022, Download Paper (Free):

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.


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Cite this article

[IEEE Style]
J. Liu, J. Tan, J. Qin and X. Xiang, "Smoke Image Recognition Method Based on the optimization of SVM parameters with Improved Fruit Fly Algorithm," KSII Transactions on Internet and Information Systems, vol. 14, no. 8, pp. 3534-3549, 2020. DOI: 10.3837/tiis.2020.08.022.

[ACM Style]
Jingwen Liu, Junshan Tan, Jiaohua Qin, and Xuyu Xiang. 2020. Smoke Image Recognition Method Based on the optimization of SVM parameters with Improved Fruit Fly Algorithm. KSII Transactions on Internet and Information Systems, 14, 8, (2020), 3534-3549. DOI: 10.3837/tiis.2020.08.022.