Application of KNN Algorithm Based on Particle Swarm Optimization in Fire Image Segmentation

In the field of fire image segmentation, most methods are based on color threshold segmentation, so different thresholds should be set according to different environments. In this process, there are too many manual operations. In order to achieve the automatic segmentation of fire images, a modified 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 influence of the initial value of K-Median algorithm on the results. Combined with Euclidean distance and correlation distance, a new similarity function is defined to improve the classification accuracy of KNN algorithm. Experiment results show the proposed algorithm has been improved both in classification accuracy and speed.