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

Liu, Jingwen ; Tan, Junshan ; Qin, Jiaohua ; Xiang, Xuyu

KSII transactions on Internet and information systems, 2020-08, Vol.14 (8), p.3534-3549 [Periódico revisado por pares]

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Título:
Smoke Image Recognition Method Based on the optimization of SVM parameters with Improved Fruit Fly Algorithm
Autor: Liu, Jingwen ; Tan, Junshan ; Qin, Jiaohua ; Xiang, Xuyu
Assuntos: fruit fly optimization algorithm ; Multiple Kernel Learning ; smoke image
É parte de: KSII transactions on Internet and information systems, 2020-08, Vol.14 (8), p.3534-3549
Notas: Korean Society for Internet Information
KISTI1.1003/JNL.JAKO202026061058003
Descrição: 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.
Editor: 한국인터넷정보학회
Idioma: Coreano

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