Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (04): 28-32.doi: 10.13475/j.fzxb.20201105706

• Fiber Materials • Previous Articles     Next Articles

Cashmere and wool classification based on sparse dictionary learning

SUN Chunhong1, DING Guangtai1,2(), FANG Kun1   

  1. 1. School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
    2. Materials Genome Institute, Shanghai University, Shanghai 200444, China
  • Received:2020-11-30 Revised:2022-01-17 Online:2022-04-15 Published:2022-04-20
  • Contact: DING Guangtai E-mail:gtding@shu.edu.cn

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Abstract:

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.

Key words: sparse representation, dictionary learning, image recognition, machine learning, cashmere, wool, fiber identification

CLC Number: 

  • TP391.4

Fig.1

Original images of cashmere and wool. (a) Cashmere optical microscope image(×500); (b) Wool optical microscope image(×500); (c) Cashmere SEM image(×1 000); (d) Wool SEM image(×1 000)"

Fig.2

Image samples after preprocessing. (a) Wool SEM image after denoising; (b) Wool optical microscope image after denoising; (c) Cashmere augmentation image 1; (d) Cashmere augmentation image 2"

Fig.3

Example diagram of LBP feature extraction"

Tab.1

Optical microscope data set"

数据集编号 数据集名称 图像种类 数量/张
1 训练集 山羊绒 4 607
2 训练集 绵羊毛 3 950
3 测试集 山羊绒 1 618
4 测试集 绵羊毛 1 626

Tab.2

Scanning electron microscope data set"

数据集编号 数据集名称 图像种类 数量/张
1 训练集 山羊绒 3 468
2 训练集 绵羊毛 4 724
3 测试集 山羊绒 1 665
4 测试集 绵羊毛 1 756

Fig.4

Steps of cashmere and wool classification based on sparse dictionary learning"

Tab.3

Classification results of SDL method under different number of dictionary items"

实验
编号		 字典
个数k		 准确率
A/%		 召回率R/
%		 查全率P/
%		 F1-score/
%
1 2 000 55.92 90.17 53.44 67.11
2 3 000 69.57 91.29 63.58 74.96
4 4 000 82.86 92.71 77.40 84.36
5 5 000 87.36 83.99 90.00 86.29

Tab.4

Comparison of classification results"

方法
编号		 方法
名称		 准确率
A/%		 召回率
R/%		 精准率
P/%		 F1-score/
%
1 LBP+SVM 48.12 14.83 44.04 22.19
2 LBP+K-SVD+SVM 49.88 99.89 49.88 66.56
3 SRC 53.32 94.19 52.52 67.44
4 SDL 61.24 80.35 58.99 68.03
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