针对图像分类场景的粒子群算法优化方法
Particle Swarm Optimization Method for Image Classification Scene
王熠 1苏孟豪 1左昊文2
作者信息
- 1. 郑州工业应用技术学院,河南郑州 451100
- 2. 中原工学院,河南郑州 450007
- 折叠
摘要
文章针对图像分类任务中特征选择的问题,提出一种基于粒子群算法的优化方法.首先,文章深入研究粒子群算法的基本原理;其次,引入粒子群优化算法进行特征选择;最后,使用支持向量机进行图像分类.实验结果表明,所提出的粒子群优化方法显著提高了图像分类的准确性,且该方法的一致性和稳健性较好.
Abstract
This paper proposes an optimization method based on particle swarm algorithm to solve the problem of feature selection in image classification tasks.First,the basic principles of particle swarm optimization is deeply studied.Then,particle swarm optimization algorithm is introduced for feature selection.Finally,support vector machine is used for image classification.The experimental results show that the proposed particle swarm optimization method significantly improves the accuracy of image classification,and the method has good consistency and robustness.
关键词
粒子群算法/特征选择/图像分类/优化方法Key words
particle swarm optimization/feature selection/image classification/optimization method引用本文复制引用
出版年
2024
NETL
NSTL
万方数据