摘要
为准确鉴别羊绒与羊毛纤维,提出了一种基于稀疏字典学习的分类方法。首先,对纤维图像进行预处理实现数据增强,获取纤维图像特征矩阵;之后,对特征矩阵进行字典学习,获取过完备字典与稀疏编码;最后,通过稀疏编码与字典实现羊绒与羊毛的分类和鉴别。该方法使用光学显微镜以及扫描电子显微镜图像作为数据集,实验结果表明,与支持向量机分类器以及基于稀疏表示的分类算法相比,本文方法的分类准确率可提高5%~10%,分类准确率最高可达到91%,可用于后续实际的羊绒与羊毛纤维分类与鉴定工作。
In order to identify cashmere and wool fibers accurately,this paper proposes a classification method based on sparse dictionary learning.Firstly,the fiber image is preprocessed to achieve data enhancement to achieve a fiber image feature matrix.Secondly,dictionary learning is performed on the feature matrix to obtain a complete dictionary and sparse coding.Finally,based on sparse coding and dictionary,the classification and identification of cashmere and wool is implemented.This method uses optical microscope images and scanning electron microscope images as data sets.Experiment results show that compared with support vector machine classifiers and sparse representation-based classifier algorithms,the classification accuracy of this method can be improved by 5%-10%,and the classification accuracy can reach up to 91%.It can be used for subsequent actual classification and identification of cashmere and wool fibers.
作者
孙春红
丁广太
方坤
SUN Chunhong;DING Guangtai;FANG Kun(School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China;Materials Genome Institute, Shanghai University, Shanghai 200444, China)
出处
《纺织学报》
EI
CAS
CSCD
北大核心
2022年第4期28-32,39,共6页
Journal of Textile Research
基金
国家重点研发计划项目(2018YFB0704400,2016YFB0700500)。
关键词
稀疏表示
字典学习
图像识别
机器学习
羊绒
羊毛
纤维鉴别
sparse representation
dictionary learning
image recognition
machine learning
cashmere
wool
fiber identification
