| 한글제목(Korean Title) |
Smoke Image Recognition Method Based on the optimization of SVM parameters with Improved Fruit Fly Algorithm |
| 영문제목(English Title) |
Smoke Image Recognition Method Based on the optimization of SVM parameters with Improved Fruit Fly Algorithm |
| 저자(Author) |
Jingwen Liu
Junshan Tan
Jiaohua Qin
Xuyu Xiang
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| 원문수록처(Citation) |
VOL 14 NO. 08 PP. 3534 ~ 3549 (2020. 08) |
한글내용
(Korean Abstract) |
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영문내용
(English 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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| 키워드(Keyword) |
smoke image
fruit fly optimization algorithm
Multiple Kernel Learning
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| 파일첨부 |
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