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Application of KNN Algorithm Based on Particle Swarm Optimization in Fire Image Segmentation
Yuanbin Wang,Jieying Ren 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.4
In the fi eld of fi re image segmentation, most methods are based on color threshold segmentation, so diff erent thresholds should be set according to diff erent environments. In this process, there are too many manual operations. In order to achieve the automatic segmentation of fi re images, a modifi ed KNN segmentation algorithm based on particle swarm optimization is proposed. Firstly, a large number of sample data is cropped, redundant samples are removed, and then an improved KNN is employed to classify image pixels. In this paper, K-Median algorithm is used to cluster samples and reduce the computation of similarity degree in KNN. In this process, Particles Swarm Optimization are adopted to avoid the infl uence of the initial value of K-Median algorithm on the results. Combined with Euclidean distance and correlation distance, a new similarity function is defi ned to improve the classifi cation accuracy of KNN algorithm. Experiment results show the proposed algorithm has been improved both in classifi cation accuracy and speed.
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