Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (07): 147-153.doi: 10.13475/j.fzxb.20191002107

• Apparel Engineering • Previous Articles     Next Articles

Young Xinjiang female hip shape characterization and prototype correction using XGBoost algorithm

LIU Tingting, XU Hong, MEI Xinyuan, LIU Yixin, XIAO Aimin()   

  1. College of Textiles and Clothing, Xinjiang University, Urumqi, Xinjiang 830046, China
  • Received:2019-10-10 Revised:2020-03-05 Online:2020-07-15 Published:2020-07-23
  • Contact: XIAO Aimin E-mail:495178065@qq.com

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Abstract:

This paper addresses the fitting issues for Xinjiang skirts through a study on the size of lower body shape. In order to determine the true shape of hip body accurately, hip data of 220 young Xinjiang females aged 18 to 25 were analyzed. Using the factor analysis and correlation coefficients mergods, 2 factor for clustering were obtained, i.e. back hip length to waist girth ratio, back hip length to hip girth ratio, and 3 hip body types were defined based on the K-means clustering method. On this basis, Python software was used to establish an XGBoost buttock discriminant model. The work involved three parts. The first thing was to compare and analyze different algorithm models and XGBoost was used to evaluate the accuracy, and the highest accuracy reached 98.4%. The second part was to,modify the intermediate skirt prototype for young Xinjiang females, where it was found that the rear hip length was 2.4 cm different from the rear hip length of the standard dress prototype, indicating that the young Xingjiang female hips is larger than that in other regions. Finally, the discriminating algorithm was applied to the data system. The outcome of the research is shown to have improved discrimination efficiency for body size, and the method provides data support for other human related fields.

Key words: young Xinjiang female, hip shape classification, body shape, XGBoost algorithm, skirt prototype correction

CLC Number: 

  • TS941.17

Fig.1

Hip length and angle measurement schematic. (a) Face of hip body;(b) Side of hip body"

Tab.1

Analysis of contribution rate of main component"

成分 旋转载荷平方和
特征根 贡献率/% 累积贡献率/%
1 5.616 33.035 33.035
2 3.195 18.797 51.832
3 2.972 17.480 69.311
4 1.326 7.798 77.110

Tab.2

Rotating composition matrix"

直接变量 成分
1 2 3 4
臀围 0.888 0.125 0.173 0.027
体重 0.880 0.244 0.128 0.055
大腿根围 0.861 -0.036 0.054 0.069
腰围 0.834 0.041 0.001 -0.056
腰厚 0.803 0.027 0.015 -0.219
臀厚 0.800 0.045 -0.042 -0.022
中腰围 0.794 0.217 0.103 0.087
腹厚 0.772 0.128 0.006 -0.135
身高 0.201 0.892 0.218 0.055
臀高 0.124 0.884 0.037 0.032
腰高 0.169 0.877 0.305 0.036
膝盖中点高 0.016 0.782 0.042 0.022
后臀长 0.059 0.123 0.964 0.104
前臀长 0.081 0.167 0.962 -0.071
侧臀长 0.077 0.192 0.936 0.061
臀突上角 -0.017 0.120 -0.093 0.796
腰侧角 -0.074 -0.021 0.165 0.762

Tab.3

Correlation coefficients and coefficients of variation"

主要因子 指标 相关指数 变异系数
围度因子 体重 0.525 0.115 5
臀围 0.523 0.054 0
大腿根围 0.451 0.073 2
腰围 0.452 0.080 5
腰厚 0.401 0.110 6
中腰围 0.416 0.074 5
臀厚 0.384 0.085 0
腹厚 0.380 0.092 3
高度因子 身高 0.598 0.033 8
臀高 0.513 0.050 5
腰高 0.594 0.046 1
膝盖中点高 0.351 0.049 4
臀长因子 后臀长 0.872 0.104 9
前臀长 0.843 0.098 6
侧臀长 0.841 0.102 7
角度因子 臀突上角 0.256 0.289 5
腰侧角 0.256 0.237 4

Tab.4

One-way ANOVA between each derived variable and key part of hip body"

项目 指标 BMI值 臀腰差/cm 臀腰比 腰围身高比 臀围身高比 后臀长腰围比 后臀长臀围比 后臀长身高比
F值 体重 2.335 2.311 3.262 5.683 2.907 2.393 3.664 1.752
腰围 2.847 4.336 18.120 6.558 11.736 5.375 2.328 1.339
臀围 1.997 2.381 7.826 21.714 1.052 1.689 19.136 1.341
身高 7.826 1.395 1.656 1.474 1.964 1.766 1.947 10.728
后臀长 0.950 0.959 0.803 0.634 0.612 12.716 95.599 139.101
P值 体重 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.034
腰围 0.008 0.000 0.000 0.000 0.000 0.000 0.000 0.170
臀围 0.001 0.000 0.000 0.000 0.404 0.009 0.000 0.169
身高 0.000 0.078 0.018 0.113 0.008 0.005 0.002 0.000
后臀长 0.589 0.546 0.847 0.853 0.917 0.000 0.000 0.000

Fig.2

Scatter plot of clustering indicators and major variables. (a) A scatterplot of clustering indicators and W;(b) A scatterplot of clustering indicators and H;(c) A scatterplot of clustering indicators and Z;(d) A scatterplot of clustering indicators and BHL"

Tab.5

Average and proportion of main indicators of three body types"

体型类别 后臀长腰围比 后臀长臀围比 占比/%
1 0.33 0.25 21
2 0.28 0.22 42
3 0.24 0.19 37

Fig.3

Three hip body shape diagram. (a)Face of hip body;(b)Side of hip body"

Tab.6

Comparison of waist and hip data mean in different regionscm"

指标 新疆地区 上海地区 东北地区
2015 2019 2014 2017 2013 2017
腰围 70.4 71.83 67.7 69.4 68.5 69.5
臀围 93.7 94.03 89.1 92.8 92.5 92.0
腰高 100.0 103.00 97.8 100.5 100.6 103.3

Fig.4

XGBoost model body size difference flow chart"

Fig.5

Comparison of revised skirt prototype and standard skirt prototype"

[1] 尹玲, 张文斌, 许才国. 基于有序样本最优分割法的女性体型分类[J]. 纺织学报, 2014,35(9):114-119.
YIN Lin, ZHANG Wenbin, XU Caiguo. Female body shape classification based on optimal segmentation method for orderly samples[J]. Journal of Textile Research, 2014,35(9):114-119.
[2] MAJA Mahnic Naglic, SLAVENKA Petrak. A method for body posture classification of three-dimensional body models in the sagittal plane[J]. Textile Research Journal, 2017,89(2):133-149.
[3] 王军, 李晓久, 潘力, 等. 东北地区青年女性腰臀部体型特征与分类[J]. 纺织学报, 2018,39(4):106-110.
WANG Jun, LI Xiaojiu, PAN Li, et al. Waist hip somatotype and classification of young women in Northeast China[J]. Journal of Textile Research, 2018,39(4):106-110.
[4] 石小强, 王宏付. 青年女性臀部高度特征分析[J]. 纺织学报, 2015,36(5):74-78.
SHI Xiaoqiang, WANG Hongfu. Analysis on characteristics about hip height of young female[J]. Journal of Textile Research, 2015,36(5):74-78.
[5] 尹玲, 夏蕾, 许才国. 基于随机森林的女性体型判别[J]. 纺织学报, 2014,35(5):113-117.
YIN Lin, XIA Lei, XU Caiguo. Female body shape prediction based on random forest[J]. Journal of Textile Research, 2014,35(5):113-117.
[6] 景晓宁, 李晓久. 朴素贝叶斯算法在女童体型判别中的应用[J]. 纺织学报, 2017,38(12):124-128.
JING Xiaoning, LI Xiaojiu. Application of Naive Bayesian method in girl's figure discrimination[J]. Journal of Textiles Research, 2017,38(12):124-128.
[7] CHEN Tianqi, GUESTRIN C. XGBoost: a scalable tree boosting system[C] //Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2016: 785-794.
[8] LI G Y, LI W, TIAN X L, et al. Short-term electricity load forecasting based on the XGBoost algorithm[J]. Smart Grid, 2017,7(4):274-285.
[9] 程朋朋, 陈道玲. 福建青年女性的人体测量与体型特点分析[J]. 北京服装学院学报(自然科学版), 2017(1):93-100.
CHENG Pengpeng, CHEN Daoling. Analysis of the human measurement and body characteristics of young women in Fujian[J]. Journal of Beijing Institute of Clothing Technology(Natural Science Edition), 2017(1):93-100.
[10] 张文斌. 服装结构设计[M]. 北京: 中国纺织出版社, 2017: 59-69.
ZHANG Wenbin. Clothing structure design [M]. Beijing: China Textile & Apparel Press, 2017: 59-69.
[11] 三吉满智子. 服装造型学[M]. 北京: 中国纺织出版社, 2006: 54-180.
TOMOKO Miyoshi. Costume styling [M]. Beijing: China Textile & Apparel Press, 2006: 54-180.
[12] 黄灿艺. 福建地区青年女性体型划分与尺寸分档[J]. 纺织学报, 2012,33(5):111-115.
HUANG Canyi. Classification of figure type and size grading of young women of Fujian province[J]. Journal of Textile Research, 2012,33(5):111-115.
[13] SU J, KE Y, KUANG C, et al. Converting lower-body features from three-dimensional body images into rules for individualized pant patterns[J]. Textile Research Journal, 2018,89(11):2199-2208.
[14] 方方, 王子英. K-Means聚类分析在人体体型分类中的应用[J]. 东华大学学报(自然科学版), 2014,40(5):593-598.
FANG Fang, WANG Ziying. Application of K-means clustering analysis in the body shape classification[J]. Journal of Donghua University(Natural Science), 2014,40(5):593-598.
[15] PEI Jie, PARK Huiju, SUSAN P Ashdown. Female breast shape categorization based on analysis of CAESAR 3D body scan data[J]. Textile Research Journal, 2018,89(4):590-611.
doi: 10.1177/0040517517753633
[16] 吴奕颖, 肖爱民. 新疆青年女性体型研究[J]. 天津纺织科技, 2015(1):63-64.
WU Yiying, XIAO Aimin. Study on the size of young women in Xinjiang[J]. Tianjin Textile Technology, 2015(1):63-64.
[17] 方方, 徐跃瑄. 基于三维数据的青年女性腰臀部形态分类分析[J]. 服装学报, 2017,2(4):294-300.
FANG Fang, XU Yuexuan. Morphological classification of the waist-hip shape of young woman based on three-dimensional data[J]. Journal of Clothing Research, 2017,2(4):294-300.
[18] 王燕, 郭元凯. 改进的XGBoost模型在股票预测中的应用[J]. 计算机工程与应用, 2019,24(9):1-10.
WANG Yan, GUO Yuankai. Application of improved XGBoost model in stock forecasting[J]. Computer Engineering and Applications, 2019,24(9):1-10.
[19] BINA Abling, KATHLEEN Magglo. Integrating: draping and drawing[M]. America: Fairchild Books Visuals, 2016: 63-70.
[20] 刘瑞璞. 服装纸样设计原理与应用[M]. 北京: 中国织出版社, 2008: 77-97.
LIU Ruipu. The principles and applications of clothing paper design [M]. Beijing: China Textile & Apparel Press, 2008: 77-97.
[1] . Young female body shape classification and prototype patterns based on wavelet coefficient [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(12): 119-123.
[2] . Application of body shape analysis in observation of clothing size adaptability [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(01): 111-115.
[3] . Female Body Shape Classification Based on the optimal segmentation method for orderly samples [J]. JOURNAL OF TEXTILE RESEARCH, 2014, 35(9): 114-0.
[4] . Female body shape prediction based on random forest [J]. JOURNAL OF TEXTILE RESEARCH, 2014, 35(5): 113-0.
[5] Juan-Feng JIN Jie SUN. Research on subdividing of young female’s hip shapes based on 3-D body measurement [J]. JOURNAL OF TEXTILE RESEARCH, 2013, 34(9): 108-0.
[6] GAN Yingjin;CHEN Dongsheng;LIU Yunjuan. Comparative study on body shape features of Korean and Chinese young women [J]. JOURNAL OF TEXTILE RESEARCH, 2007, 28(3): 88-91.
[7] GE Yan;LIU Guolian. Study on photogrammetric measurement of body in apparel industry [J]. JOURNAL OF TEXTILE RESEARCH, 2007, 28(10): 78-81.
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