现代电子技术2017，Vol.40Issue(17)：47-50,4.DOI:10.16652/j.issn.1004-373x.2017.17.012
粒子群优化算法选择特征的运动图像分类
Moving image classification based on particle swarm optimization algorithm selecting features
摘要
Abstract
In order to improve the effect of image classification and realize the accurate image classification,a moving image classification method based on particle swarm optimization algorithm selecting features is proposed. The current research status of the moving image classification methods is analyzed to extract the images of different types. The particle swarm optimization algorithm is used to select the optimal feature to compose the feature vector. The feature vector machine is taken as the input of neural network to classify the moving images. The classification experiments of specific images were adopted to make verification. The experimental results show that the method can describe the categories information of different moving images,reduce the classification error of the images,avoid the defects of other image classification methods,and improve the overall image classifi-cation accuracy.关键词
运动图像/特征选择/粒子群算法/图像分类Key words
moving image/feature selection/particle swarm optimization algorithm/image classification分类
信息技术与安全科学引用本文复制引用
吴雪..粒子群优化算法选择特征的运动图像分类[J].现代电子技术,2017,40(17):47-50,4.
