Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (06): 146-152.doi: 10.13475/j.fzxb.20200905607

• Apparel Engineering • Previous Articles     Next Articles

Research on clothing collar types based on complex network extraction and support vector machine classification

XU Zengbo(), ZHANG Ling, ZHANG Yanhong, CHEN Guiqing   

  1. College of Fashion, Shanghai University of Engineering Science, Shanghai 201600, China
  • Received:2020-09-21 Revised:2021-03-03 Online:2021-06-15 Published:2021-06-28

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Abstract:

In order to achieve automatic style search in clothing pattern-making, this research took the structural features of clothing collar styles as working object, using clothing round-neck images as an example. The paper described and extracted complex network features by constructing a complex network, and the support vector machine model was used to classify images of 8 types of collars. The experimental results show that the average classification accuracy of the samples as a whole is 98%, and the average classification accuracy of each category is above 96%. Among them, the average classification accuracy rate for the round collar samples is 100%. At the same time, in order to evaluate the anti-noise performance of the feature extraction algorithm, after adding a certain degree of salt and pepper noise and Gaussian noise to the image of the original sample library, the overall classification accuracy of the sample fluctuates around 80%, indicating that the support vector machine classification method is suitable for image recognition with a certain degree of noise. To conclude, the extraction and classification accuracy of clothing collar research based on complex network extraction and support vector machine classification is high, and the classification results are relatively stable.

Key words: complex network, feature extraction, collar type classification, support vector machine model, clothing design

CLC Number: 

  • TS941.2

Tab.1

Classification of image feature extraction methods"

分类 描述
幅度特征 图像像素灰度值、三色值、频谱值等表示的幅值特征是最基本的图像特征
统计特征 将图像当作一个二维随机过程的一次实现,如图像的灰度直方图、均值、方差、偏差、峰度、能量、协方差等
变化系数
特征 		对于具有唯一性的任何变换,其变换域系数所决定的亮度图像,都和原空间域图像是等价的
边界特征 以灰度和三色值表示的亮度突变或者断续,成为图像中的亮度边界点
拓扑结构
特征 		它是只要图形不撕裂或折叠,是不受任何畸变影响的结构特点,这些特点既不受距离的影响,也不受基于距离测量的其他特点的影响
几何形状
特征 		目标物体的几何特征,如周长、面积、弦长等

Fig.1

Initial network construction. (a) Target part;(b) Target edge; (c) Uniform sampling; (d) Initial network"

Fig.2

Complex network model evolution with different thresholds"

Fig.3

Trend chart of different characteristics with threshold.(a) Maximum and average thresholds;(b)Average joint degree, entropy and energy threshold;(c)Local and global clustering coefficients and thresholds of average path length"

Tab.2

Type and number of experimental samples"

衣领款式 类别标签 样本总数 训练样本量 测试样本量
圆领 1# 60 45 15
V领 2# 60 45 15
方形领 3# 60 45 15
立领 4# 60 45 15
衬领 5# 60 45 15
平驳领 6# 60 45 15
连驳领 7# 60 45 15
戗驳领 8# 60 45 15

Tab.3

Type and number of experimental samples"

核函数类型 平均分类准确率/% svmtrain参数
linear 97.50 ‘-c2-g1-t0’
polynomial 96.92 ‘-c2-g1-t1’
radial basis function 95.58 ‘-c2-g1-t2’
sigmoid 8.92 ‘-c2-g1-t3’

Fig.4

Different category classification map based on SVM.(a) 2# and 3#;(b) 1# and 4#;(c) 3# and 8#;(d) 1#, 5# and 6# "

Tab.4

Type and number of experimental samples"

实验次数 圆领 V领 方形领 立领 衬衫领 平驳领 连驳领 戗驳领 整体准确率
1 1.00 1.00 1.00 1.00 1.00 0.93 0.87 0.95 0.97
2 1.00 0.93 1.00 1.00 1.00 1.00 1.00 1.00 0.99
3 1.00 1.00 1.00 1.00 1.00 0.94 1.00 0.94 0.98
4 1.00 0.95 1.00 1.00 1.00 0.88 1.00 0.92 0.97
5 1.00 1.00 1.00 0.94 0.92 0.92 1.00 1.00 0.98
6 1.00 1.00 1.00 1.00 0.93 0.94 0.86 1.00 0.97
7 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
8 1.00 0.90 1.00 0.94 1.00 1.00 1.00 0.88 0.97
9 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.92 0.99
10 1.00 1.00 0.93 1.00 1.00 1.00 0.94 1.00 0.98
平均准确率 1.00 0.98 0.99 0.99 0.98 0.96 0.97 0.96 0.98

Fig.5

Trend chart of different characteristics with threshold.(a) Original image;(b) Noise 10%; (c) Noise 15%;(d) Noise 20%"

Fig.6

Add Gaussian noise images of different densities. (a) Original image; (b) Variance 0.01; (c) Variance 0.05; (d) Variance 0.1"

Fig.7

Add classification accuracy of different densities of salt and pepper noise images. (a) Overall classification accuracy of salt and pepper;(b) Average accuracy of salt and pepper classification"

Fig.8

Add different variance of Gaussian noise image classification accuracy.(a) Gaussian overall classification accuracy;(b) Gaussian average classification accuracy"

Fig.9

Comparison of classification accuracy of this method with Hu invariant moments and HOG.(a) Overall classification accuracy of the three methods;(b) Average classification accuracy of three methods"

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