现代电子技术2017，Vol.40Issue(19)：79-82,4.DOI:10.16652/j.issn.1004-373x.2017.19.020
特征选择和聚类分析的图像分类模型
Image classification model based on feature selection and clustering analysis
摘要
Abstract
Aiming at the problem that the current image classification model cannot meet the requirements of practical ap-plication,an image classification model based on feature selection and clustering analysis is proposed to obtain better results of image classification. First,the original image features are extracted and the principal component analysis(PCA)is used to se-lect the image features. Then,the clustering analysis algorithm is adopted to process image samples,select samples relevant to the images waiting for classification,and reduce the scale of training samples. Finally,the support vector machine is used to construct the image classifier and classification experiments are carried out for images in the standard image database. The re-sults show that,by using this model,the features and training samples of image classification are reduced,the image classifica-tion modeling is sped up,and the correctness of image classification is obviously higher than that of other image classification models.关键词
图像处理/原始特征/聚类分析算法/图像分类器Key words
image processing/original feature/clustering analysis algorithm/image classifier分类
信息技术与安全科学引用本文复制引用
彭娟..特征选择和聚类分析的图像分类模型[J].现代电子技术,2017,40(19):79-82,4.基金项目
重庆市教委科学技术研究项目(KJ1502301) （KJ1502301）
