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

RichHTML

9

PDF (PC)

126

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
[1] CHEN Y, ZENG X, HAPPIETTE M, et al. Fuzzy applications in industrial engineering[M]. Berlin: Springer, 2006: 367-379.
[2] LIU K X, ZENG X Y, BRUNIAUX P, et al. Fit evaluation of virtual garment try-on by learning from digital pressure data[J]. Knowledge-Based Systems, 2017,133:174-182.
doi: 10.1016/j.knosys.2017.07.007
[3] LI J, YE J, WANG Y, et al. Technical section: fitting 3D garment models onto individual human models[J]. Computers & Graphics, 2010,34(6):742-755.
[4] WANG L C, ZENG X Y, KOEHL L, et al. Intelligent fashion recommender system: fuzzy logic in personalized garment design[J]. IEEE Transactions on Human-Machine Systems, 2015,45(1):95-109.
doi: 10.1109/THMS.2014.2364398
[5] 孙守迁, 徐爱国, 黄琦, 等. 基于角色几何碰撞体估计的实时服装仿真[J]. 软件学报, 2007,18(11):2921-2931.
SUN S Q, XU A G, HUANG Q, et al. Real-Time garment simulation based on geometrically approximating character with colliding objects[J]. Journal of Software, 2007,18(11):2921-2931.
[6] 孟祥令, 张渭源. 服装压力舒适性的研究进展[J]. 纺织学报, 2006,27(7):109-112.
MENG Xiangling, ZHANG Weiyuan. Progress of study on pressure comfort of clothing[J]. Journal of Textile Research, 2006,27(7):109-112.
[7] SONG H K, ASHDOWN S P. Investigation of the validity of 3-D virtual fitting for pants[J]. Clothing and Textiles Research Journal, 2015,33(4):314-330.
doi: 10.1177/0887302X15592472
[8] MENG Y W, MOK P Y, JIN X G. Interactive virtual try-on clothing design systems[J]. Computer-Aided Design, 2010,42(4):310-321.
doi: 10.1016/j.cad.2009.12.004
[9] MENG Y W, MOK P Y, JIN X G. Computer aided clothing pattern design with 3D editing and pattern alteration[J]. Computer-Aided Design, 2012,44(8):721-734.
doi: 10.1016/j.cad.2012.03.006
[10] SAYEM A S M, KENNON R, CLARKE N. 3D CAD systems for the clothing industry[J]. International Journal of Fashion Design, Technology and Education, 2010,3(2):45-53.
doi: 10.1080/17543261003689888
[11] LIU K, KAMALHA E, WANG J, et al. Optimization design of cycling clothes' patterns based on digital clothing pressures[J]. Fibers Polymers, 2016,17(9):1522-1529.
doi: 10.1007/s12221-016-6402-2
[12] LEE W, KO H S. Heuristic misfit reduction: A programmable approach for 3D garment fit customiza-tion[J]. Computers & Graphics-Uk, 2018,71:1-13.
[13] HINDS B K, MCCARTNEY J J V C. Interactive garment design[J]. Visual Computer, 1990,6(2):53-61.
doi: 10.1007/BF01901066
[14] TAN C, SUN F, KONG T, et al. A survey on deep transfer learning[C]// The 27th International conference on artificial neural networks. Cham: Springer, 2018: 270-279.
[15] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recogni-tion[C/OL]. San Diego: Proceedings of the 2015 International Conference on Learning Representations. 2015: 1150-1210. [2020-03-10] https://arxiv.org/abs/1409.1556.htm.
[16] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]// Proceedings of the 25th international conference on neural information processing systems. New York: Curran Associates, Inc., 2012: 1097-1105.
[17] CLO Virtual Fashion Inc. CLO 3D[CP/OL]. [2020-03-10]. https://www.clo3d.com/.htm.
[18] OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(7):971-987.
doi: 10.1109/TPAMI.2002.1017623
[19] SUYKENS J A K, VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999,9(3):293-300.
doi: 10.1023/A:1018628609742
[1] WANG Yiwen, LUO Ronglei, KANG Yuzhe. Automatic measurement of key dimensions for Han-style costumes based on use of convolutional neural network [J]. Journal of Textile Research, 2020, 41(12): 124-129.
[2] GONG Xue, YUAN Li, LIU Junping, YANG Yali, LIU Muli, KE Zhengtao, YAN Yuchen. Recognition of colored spun fabric interlacing point based on mixed color space and multiple kernel learning [J]. Journal of Textile Research, 2020, 41(05): 58-65.
[3] SHI Kangjun, WANG Jing'an, GAO Weidong. Objective smoothness evaluation of fabric based on Fourier spectral features [J]. Journal of Textile Research, 2019, 40(11): 50-56.
[4] TAO Kaixin, YU Chengbing, HOU Qi'ao, WU Congjie, LIU Yinfeng. Wet-steaming dyeing prediction model of cotton knitted fabric with reactive dye based on least squares support vector machine [J]. Journal of Textile Research, 2019, 40(07): 169-173.
[5] ZHU Hao, DING Hui, SHANG Yuanyuan, SHAO Zhuhong. Defect detection algorithm for multiple texture hierarchical fusion fabric [J]. Journal of Textile Research, 2019, 40(06): 117-124.
[6] XING Wenyu, DENG Na, XIN Binjie, YU Chen. Identification of wool and cashmere based on multi-feature fusion image analysis technology [J]. Journal of Textile Research, 2019, 40(03): 146-152.
[7] . Single component textile identification based on continuous projection algorithm and least squares support vector machine [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(08): 46-51.
[8] . Prediction on filtration performance of melt blown nonwoven fabric based on rough set theory and support vector machine [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(06): 142-148.
[9] . Global and local diversity features-fused colorimetry index testing and evaluation of colored spun yarns [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(02): 157-164.
[10] . Clothing style recognition approach using Fourier descriptors and support vector machines [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(05): 122-127.
[11] . Computer recognition and simulation  of fabric color from matching to standard color chip [J]. Journal of Textile Research, 2016, 37(05): 150-154.
[12] YING Le-Bin, DAI Lian-Kui, WU Jian-Jian, SUN Guo-Jun. Identification of Single Cotton Fiber and Flax Fiber Based on Microscopic Fiber Images in the Longitudinal View [J]. JOURNAL OF TEXTILE RESEARCH, 2012, 33(4): 12-18.
[13] ZHANG Xiumei;SUN Yongjian;GUO Liangwei. Forecasting model for apparel demand based on improved least squares support vector machine (LS-SVM)oriented to mass customization [J]. JOURNAL OF TEXTILE RESEARCH, 2010, 31(5): 141-145.
[14] YANG Gang;;ZHONG Yueqi;. It is not rare for penetration phenomena to occur between garment and mannequin when 3-D garment is dressed onto various mannequins. In order to enhance the reusability of the 3-D scanned garment model, an algorithm based on same layer penetration compensation has been proposed, in which, the penetration detection and compensation between garment and mannequin are expressed respectively by the crossover and compensation between garment vertex and body triangle, and between garment edge/body triangle. The over-deformation is compensated via the position adjustment procedure. Experimental results verify that this method is an efficient approach for reusing 3-D scanned garment models. [J]. JOURNAL OF TEXTILE RESEARCH, 2010, 31(10): 134-138.
[15] LIN Zhigui; FANG Wei; HUANG Weizhi. Analysis of fabric drape evaluation based on support vector machine [J]. JOURNAL OF TEXTILE RESEARCH, 2009, 30(01): 51-54.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd