计算技术与自动化2017，Vol.36Issue(1)：98-102,5.DOI:10.3969/j.issn.1003-6199.2017.01.020
基于ELM和证据理论的纹理图像分类
Texture Image Classification Based on ELM and Evidence
易丐 1李国进 1王祥铜1
作者信息
- 1. 广西大学 电气工程学院,广西 南宁 530004
- 折叠
摘要
Abstract
There are different methods for texture image classification at present.In this paper,we propose a multi classifier decision level fusion model based on D-S evidence theory and extreme learning machine,which is used to classify the texture image.we use three different methods to extract texturefeature of texture imageto obtain more more comprehensive texture forms and build classifiers using extreme learning machine based on the each feature vector.Finally the classifiers were fused by D-S evidence theory since the advantage of uncertainty representation,measure and the aspect of combination.To the problem of getting the basic probability assignment function(BPAF)in D-S evidence theory is hard,we used ELM to construct the basic probability assignment function,because the extreme learning machine have advantages of fast learning speed,good generalization performance and produce the uniqueness of the optimalsolution.The experimental results show that this method has higher recognition accuracy than a single classifier,and the uncertainty of the recognition is reduced.关键词
纹理图像分类/特征提取/D-S证据理论/极限学习机/基本概率赋值函数Key words
texture image classification/feature extraction/D-S evidence theory/extreme learning machine/BPAF分类
信息技术与安全科学引用本文复制引用
易丐,李国进,王祥铜..基于ELM和证据理论的纹理图像分类[J].计算技术与自动化,2017,36(1):98-102,5.
