Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (06): 125-131.doi: 10.13475/j.fzxb.20191101007

• Apparel Engineering • Previous Articles     Next Articles

Clothing comfort evaluation based on transfer learning and support vector machine

XIA Haibang, HUANG Hongyun, DING Zuohua()   

  1. College of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
  • Received:2019-11-04 Revised:2020-02-29 Online:2020-06-15 Published:2020-06-28
  • Contact: DING Zuohua E-mail:zouhuading@hotmail.com

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Abstract:

The traditional methods for clothing comfort evaluation is carried out through the try-on effect of the garment, which requires much time but with low evaluation accuracy. This paper presented a clothing comfort evaluation model learning from clothing patterns based on the transfer learning and support vector machine fast and accurately. The sizes of mannequins and the graphs of garment patterns were firstly collected, and the graphs of garment patterns were improved by using transfer learning to create garment pattern database. Then, a comfort label acquisition method was presented based on Virtual Try-On, adding comfort label to the corresponding graph of garment pattern. Following that, local binary pattern was extracted from the graph of garment pattern, and it was combined with the sizes of the corresponding mannequins to form clothing comfort feature vector. Finally, the clothing comfort feature vectors of garment pattern database were extracted to train the support vector machine. This exercise shows that the accuracy and average time to evaluate clothing comfort using this method are 0.834 and 12 s respectively, representing satisfactory accuracy and efficiency.

Key words: clothing comfort evaluation, transfer learning, virtual try-on, feature fusion, support vector machine

CLC Number: 

  • TS941

Fig.1

Example of mannequin"

Tab.1

Model size of our mannequinsmm"

模型序列 下半身长 腰围 臀围 膝盖围 小腿长
0 897.7 600.0 834.2 297.8 365.5
1 897.7 619.9 854.3 303.9 356.5
2 938.6 640.0 880.8 313.3 374.9
3 938.5 660.0 896.9 318.8 374.9
4 938.5 681.0 917.8 325.0 374.8
5 979.4 700.0 939.5 333.7 393.3
6 979.4 720.1 958.5 339.6 393.3
7 1 020.1 740.0 980.2 348.1 411.7
8 1 020.2 760.0 1 001.4 354.6 411.7

Fig.2

Overview of processing clothing patterns using pre-trained VGG"

Fig.3

Comparison between initial clothing patterns and clothing patterns processed by pre-trained VGG-19. (a) Graph of garment pattern; (b) Graph of garment pattern processed by VGG-19"

Fig.4

Fitting scene of CLO 3D"

Tab.2

Evaluation indexes of clothing try-on"

样板序列 ACW/% AT/%
0 0 12.2
1 0.2 8.0
2 0 7.9
3 0.1 4.5
4 0.2 3.4
5 0 3.2
6 0.1 1.4
7 0.1 1.2
8 0 11.2
… … …
68 0 2.1
69 0 2.1
70 0.2 3.9
71 0.1 1.1

Fig.5

Try-on comfort distribution map"

Fig.6

Feature visualization of clothing pattern diagram"

Tab.3

Comparison between transfer learning and no transfer learning"

评估方法 完备化处理 准确率
SVM NB
HOG 否 0.749 0.674
LBP+HOG 否 0.796 0.779
LBP 否 0.778 0.392
HOG 是 0.811 0.378
LBP+HOG 是 0.768 0.760
LBP(本文方法) 是 0.834 0.467

Tab.4

Comparison between our clothing comfort evaluation method and the other ones"

评估方法 准确率 时间/s
SVM NB
CLO 3D[17] 16
Liu[2] 0.757 0.705 85
HOG 0.811 0.378 11
LBP+HOG 0.768 0.760 13
LBP(本文方法) 0.834 0.467 12
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