Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (05): 58-65.doi: 10.13475/j.fzxb.20190804708

• Textile Engineering • Previous Articles     Next Articles

Recognition of colored spun fabric interlacing point based on mixed color space and multiple kernel learning

GONG Xue1, YUAN Li1,2(), LIU Junping3, YANG Yali1, LIU Muli1, KE Zhengtao1, YAN Yuchen4   

  1. 1. School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan, Hubei 430200, China
    2. State Key Laboratory for Hubei New Textile Materials and Advanced Processing Technology, Wuhan Textile University,Wuhan, Hubei 430200, China
    3. School of Mathematics and Computer Science, Wuhan Textile University, Wuhan,Hubei 430200, China
    4. Electronic Information School, Wuhan University, Wuhan, Hubei 430072, China
  • Received:2019-08-08 Revised:2020-02-18 Online:2020-05-15 Published:2020-06-02
  • Contact: YUAN Li E-mail:yuanli@wtu.edu.cn

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Abstract:

Aiming at the difficulty in extracting feature parameters of colored fabric interlacing points, an automatic recognition algorithm for such interlacing points based on mixed color space and multiple kernel learning was established. Firstly, the channel having the same color properties among the three-color spaces of YUV, HSV and Lab was fused to construct a mixed color space. On this basis, the local texture features and the third-order color moment features of the image of colored fabric interlacing points were extracted to represent the interlacing points. Finally, support vector machine was constructed by multi-kernel learning algorithm to recognize interlacing point features. The experimental results indicate that the established recognition algorithm can not only effectively recognize the interlacing points in plain, twill and satin weave fabrics, but also has ideal robustness and universality for the adjustment of fabric components and yarn forming process. The average recognition rate achieved in this research reaches 91.2%.

Key words: colored spun fabric, interlacing point recognition, mixed color space, multiple kernel learning, support vector machine

CLC Number: 

  • TS101.9

Tab.1

Parameter table of the first batch of colored spun fabric samples"

样本编号 捻系数 染色纤维质量分数/%
白色 红色 绿色
SS41A 330 30.00 35.00 35.00
SS43A 370 30.00 35.00 35.00
SS51A 330 30.00 34.75 35.25
SS53A 370 30.00 34.75 35.25
SS61A 330 30.00 34.50 35.50
SS63A 370 30.00 34.50 35.50
SS71A 330 30.00 34.00 36.00
SS73A 370 30.00 34.00 36.00
SS81A 330 30.00 33.00 37.00
SS83A 370 30.00 33.00 37.00
SS41D 330 30.00 35.00 35.00
SS43D 370 30.00 35.00 35.00
SS51D 330 30.00 34.75 35.25
SS53D 370 30.00 34.75 35.25
SS61D 330 30.00 34.50 35.50
SS63D 370 30.00 34.50 35.50
SS71D 330 30.00 34.00 36.00
SS73D 370 30.00 34.00 36.00
SS81D 330 30.00 33.00 37.00
SS83D 370 30.00 33.00 37.00
SS41F 330 30.00 35.00 35.00
SS43F 370 30.00 35.00 35.00
SS51F 330 30.00 34.75 35.25
SS53F 370 30.00 34.75 35.25
SS61F 330 30.00 34.50 35.50
SS63F 370 30.00 34.50 35.50
SS71F 330 30.00 34.00 36.00
SS73F 370 30.00 34.00 36.00
SS81F 330 30.00 33.00 37.00
SS83F 370 30.00 33.00 37.00

Fig.1

Experimental samples of some colored spun fabrics"

Tab.2

Parameter table of the second batch of colored spun fabric samples"

样本编号 染色纤维质量分数/% 说明
白色 大红或黑色 金黄或蓝色
17001 95.05 3.93 1.02 无
17004 93.60 3.90 2.50 无
17005 93.10 3.90 3.00 无
17006 94.96 3.06 1.98 无
17008 94.10 3.90 2.02 无
17008-1 94.10 3.90 2.02 捻系数300
17014 93.75 4.70 1.55 无
17015 94.01 3.53 2.46 无
17016 94.10 3.90 2.00 短绒棉
17017 94.10 3.90 2.00 长绒棉
17018 92.00 4.00 4.00 无
17020 88.00 4.00 8.00 无
17021 90.00 2.00 8.00 无
17022 89.00 3.00 8.00 无
17023 88.10 4.00 7.90 无
17024 91.00 3.00 6.00 无
17025 92.00 2.00 6.00 无
17026 88.00 4.00 8.00 捻系数54
17027 91.00 3.00 6.00 无
17028 92.00 2.00 6.00 无

Fig.2

Experimental samples of some colored spun fabrics"

Tab.3

Weight parameter optimization of kernel function"

权重
(高斯∶线性)		 各类织物中组织点的识别率/% 组织点的
平均识别
率/%
平纹 斜纹 缎纹
0.0∶1.0 88.1 87.4 65.2 80.23
0.1∶0.9 93.1 92.4 71.7 85.73
0.2∶0.8 93.2 91.4 74.8 86.47
0.3∶0.7 93.7 91.0 74.5 86.40
0.4∶0.6 93.3 89.6 74.1 85.67
0.5∶0.5 93.7 90.4 76.7 86.93
0.6∶0.4 94.5 92.8 76.7 88.00
0.7∶0.3 93.9 91.8 76.7 87.47
0.8∶0.2 94.1 94.4 81.0 89.83
0.9∶0.1 94.5 95.4 83.7 91.20
1.0∶0.0 92.7 95.8 83.0 90.50

Tab.4

Recognition results with low twist factor(experiment one)"

组织
结构		 样本
编号		 白色、红色、绿色
纤维质量比		 组织点
识别率/%		 组织点
平均识
别率/%
平
纹		 SS41A 30.00∶35.00∶35.00 98 95.2
SS51A 30.00∶34.75:35.25 98
SS61A 30.00∶34.50:35.50 93
SS71A 30.00∶34.00∶36.00 92
SS81A 30.00∶33.00∶37.00 95
斜
纹		 SS41D 30.00∶35.00∶35.00 98 96.8
SS51D 30.00∶34.75:35.25 100
SS61D 30.00∶34.50:35.50 90
SS71D 30.00∶34.00∶36.00 98
SS81D 30.00∶33.00:37.00 98
缎
纹		 SS41F 30.00∶35.00∶35.00 84 83.4
SS51F 30.00∶34.75:35.25 86
SS61F 30.00∶34.50:35.50 81
SS71F 30.00∶34.00∶36.00 82
SS81F 30.00∶33.00∶37.00 84

Tab.5

Recognition results with high twist factor(experiment two)"

组织
结构		 样本
编号		 白色、红色、绿色
纤维质量比		 组织点
识别率/
%		 组织点
平均识
别率/%
平
纹		 SS43A 30.00∶35.00∶35.00 94 93.8
SS53A 30.00∶34.75∶35.25 97
SS63A 30.00∶34.50∶35.50 89
SS73A 30.00∶34.00∶36.00 93
SS83A 30.00∶33.00∶37.00 96
斜
纹		 SS43D 30.00∶35.00∶35.00 94 94.0
SS53D 30.00∶34.75∶35.25 94
SS63D 30.00∶34.50∶35.50 92
SS73D 30.00∶34.00∶36.00 96
SS83D 30.00∶33.00∶37.00 94
缎
纹		 SS43F 30.00∶35.00∶35.00 84 84.0
SS53F 30.00∶34.75∶35.25 81
SS63F 30.00∶34.50∶35.50 83
SS73F 30.00∶34.00∶36.00 89
SS83F 30.00∶33.00∶37.00 83

Fig.3

Partial samples, segmentation image of interlacing point and pattern grid. (a) Plain weave;(b) Twill weave; (c) Satin weave"

Tab.6

Recognition results of the second batch of colored spun fabric interlacing points"

样本
编号		 白色、大红、金黄
纤维质量比		 组织点
识别率/%		 组织点平均
识别率/%		 样本
编号		 白色、黑色、蓝色
纤维质量比		 组织点
识别率/%		 组织点平均
识别率/%
17001 95.05∶3.93∶1.02 96 95.8 17018 92.00∶4.00∶4.00 93 93.5
17004 93.60∶3.90∶2.50 98 17020 88.00∶4.00∶8.00 94
17005 93.10∶3.90∶3.00 96 17021 90.00∶2.00∶8.00 88
17006 94.96∶3.06∶1.98 96 17022 89.00∶3.00∶8.00 90
17008 94.10∶3.90∶2.02 97 17023 88.10∶4.00∶7.90 90
17008-1 94.10∶3.90∶2.02 93 17024 91.00∶3.00∶6.00 92
17014 93.75∶4.70∶1.55 97 17025 92.00∶2.00∶6.00 94
17015 94.01∶3.53∶2.46 95 17026 88.00∶4.00∶8.00 96
17016 94.10∶3.90∶2.00 95 17027 91.00∶3.00∶6.00 98
17017 94.10∶3.90∶2.00 95 17028 92.00∶2.00∶6.00 100

Fig.4

Partial samples, segmentation image of interlacing point and pattern grid"

Tab.7

Recognition results of the first batch of experimental samples using different methods"

实验
方法		 各类织物中组织点的识别率/% 组织点的
平均识别
率/%
平纹 斜纹 缎纹
方法1 86.4 92.0 85.2 87.87
方法2 66.4 93.4 73.0 77.60
方法3 92.7 95.8 83.0 90.50
本文方法 94.5 95.4 83.7 91.20

Tab.8

Recognition results of the second batch of experimental samples using different methods"

实验
方法		 各类织物中组织点的识别率/% 组织点的
平均识别
率/%
纤维的颜色组成
白色、大红、金黄		 纤维的颜色组成
白色、黑色、蓝色
方法1 89.2 92.8 91.00
方法2 55.0 59.0 57.00
方法3 90.0 94.8 92.40
本文方法 95.8 93.5 94.65
[1] 张瑞, 辛斌杰. 基于图像处理技术的织物组织识别研究现状[J]. 棉纺织技术, 2015,45(11):80-84.
ZHANG Rui, XIN Binjie. Research status of fabric weave identification based on image processing technology[J]. Cotton Textile Technology, 2015,45(11):80-84.
[2] 常利利, 马俊, 邓中民, 等. 基于灰度共生矩阵的织物组织结构差异分析[J]. 纺织学报, 2008,29(10):43-46.
CHANG Lili, MA Jun, DENG Zhongmin, et al. Study on identification of fabric texture based on gray-level co-occurrence matric[J]. Journal of Textile Research, 2008,29(10):43-46.
[3] 景军锋, 邓淇英, 李鹏飞, 等. LBP和GCLM融合的织物组织结构分类[J]. 电子测量与仪器学报, 2015,29(9):1406-1412.
JING Junfeng, DENG Qiying, LI Pengfei, et al. Fabric structure classification based on LBP and GLCM fusion[J]. Journal of Electronoc Measurement and Instrumention, 2015,29(9):1406-1412.
[4] 商琳, 杨育彬, 王亮, 等. 基于颜色共生矩阵的纹理检索算法MCM[J]. 南京大学学报, 2004,40(5):540-547.
SHANG Lin, YANG Yubin, WANG Liang, et al. An image texture retrieval algorithm based on color co-occurrence matrix[J]. Journal of Nanjing University, 2004,40(5):540-547.
[5] 孙佳理. 织物组织自动识别关键技术研究[D]. 杭州:浙江大学, 2015: 3-26.
SUN Jiali. Research of key algorithms used in automatic recognition of woven fabric structures[D]. Hangzhou: Zhejiang University, 2015: 3-26.
[6] 丁莹, 钱锋, 范静涛, 等. 基于不同颜色空间的运动目标检测算法分析[J]. 长春理工大学学报(自然科学版), 2012,35(4):1-4.
DING Ying, QIAN Feng, FAN Jingtao, et al. Study on moving object detection algorithm based on different color space[J]. Journal of Changchun University of Science and Technology(Natural Science Edition), 2012,35(4):1-4.
[7] 庞晓敏, 闵子建, 阚江明. 基于HIS和LAB颜色空间的彩色图像分割[J]. 广西大学学报(自然科学版), 2011,36(6):976-980.
PANG Xiaomin, MIN Zijian, KAN Jiangming. Color image segmentation based on HIS and LAB color space[J]. Journal of Guangxi University(Natural Science Edition), 2011,36(6):976-980.
[8] 刘琼, 史诺. 基于Lab和YUV颜色空间的农田图像分割方法[J]. 国外电子测量技术, 2015,34(4):39-42.
LIU Qiong, SHI Nuo. Farmland image segmentation based on Lab and YUV color spaces[J]. Foreign Electronic Measurement Technology, 2015,34(4):39-42.
[9] 王民, 王静, 张立材, 等. 基于混合色彩空间的分块颜色特征提取算法[J]. 激光与光电子学进展, 2018,55(1):258-264.
WANG Min, WANG Jing, ZHANG Licai, et al. Block color feature extraction algorithm based on mixed color space[J]. Laser and Optoelectronics Progress, 2018,55(1):258-264.
[10] 袁理, 代乔民, 付顺林, 等. 结合全局与局部多样性特征的色纺纱色度学指标测试与评价[J]. 纺织学报, 2018,39(2):157-164.
YUAN Li, DAI Qiaomin, FU Shunlin, et al. Global and local diversity features-fused colorimetry index testing and evaluation of colored spun yarns[J]. Journal of Textile Research, 2018,39(2):157-164.
[11] 包晓敏, 曹作宝, 汪亚明, 等. 基于嗅觉神经网络的组织识别[J]. 纺织学报, 2011,32(4):52-56.
BAO Xiaomin, CAO Zuobao, WANG Yaming, et al. Fabric pattern recognition based on olfactory neural network[J]. Journal of Textile Research, 2011,32(4):52-56.
[12] 孙辉, 许洁萍, 刘彬彬. 基于多核学习支持向量机的音乐流派分类[J]. 计算机应用, 2015,35(6):1753-1756.
doi: 10.11772/j.issn.1001-9081.2015.06.1753
SUN Hui, XU Jieping LIU Binbin. Music genre classification based on multiple kernel learning and support vector machine[J]. Journal of Computer Applications, 2015,35(6):1753-1756.
doi: 10.11772/j.issn.1001-9081.2015.06.1753
[13] 钟志鹏, 张立保. 基于多核学习特征融合的人脸表情识别[J]. 计算机应用, 2015,35(S2):245-249.
ZHONG Zhipeng, ZHANG Libao. Facial expression recognition based on multi-feature fusion by multi-kernel SVM[J]. Journal of Computer Applications, 2015,35(S2):245-249.
[14] SHEN Jiajia, ZHOU Xiang. Spectrophotometric colour matching algorithm for top-dyed mélange yarn based on an artificial neural network[J]. Coloration Technology, 2017,133:341-346.
doi: 10.1111/cote.2017.133.issue-4
[15] WEI Chunao, WAN Xiaoxia, LI Junfeng. Color prediction model for pre-colored fiber blends based on modified Stearns-Noechel function[J]. Dyes and Pigments, 2017,147:544-551.
doi: 10.1016/j.dyepig.2017.08.012
[16] 袁理, 代乔民, 付顺林, 等. 基于原色纤维混配色织物的呈色特性与影响因素分析[J]. 纺织学报, 2018,39(10):38-43.
YUAN Li, DAI Qiaomin, FU Shunlin, et al. Analysis of color rendering characteristics and influencing factors of primary color fiber mixed color fabrics[J]. Journal of Textile Research, 2018,39(10):38-43.
doi: 10.1177/004051756903900107
[17] 王国德, 张培林, 任国全, 等. 融合LBP和GLCM 的纹理特征提取方法[J]. 计算机工程, 2012,38(11):199-201.
doi: 10.3969/j.issn.1000-3428.2012.11.061
WANG Guode, ZHANG Peilin, REN Guoquan, et al. Texture feature extraction method fused with LBP and GLCM[J]. Computer Engineering, 2012,38(11):199-201.
doi: 10.3969/j.issn.1000-3428.2012.11.061
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