Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (04): 149-154.doi: 10.13475/j.fzxb.20200804906

• Apparel Engineering • Previous Articles     Next Articles

Cognitive evaluation of men's suit style based on image scales

LI Qianwen1,2, WANG Jianping1,2,3(), YANG Yalan1,2, ZHANG Bingjie1,2, LI Zhilin1,2, WANG Li1,2   

  1. 1. College of Fashion and Design, Donghua University, Shanghai 200051, China
    2. Key Laboratory of Clothing Design and Technology, Ministry of Education, Donghua University, Shanghai 200051, China
    3. Shanghai International Institute of Design & Innovation, Tongji University, Shanghai 200092, China
  • Received:2020-08-10 Revised:2021-01-17 Online:2021-04-15 Published:2021-04-20
  • Contact: WANG Jianping E-mail:wangjp@dhu.edu.cn

Abstract:

In view of the lengthy descriptive words used for various types of clothing, this paper took the classic category of clothing-men's suit as an example to carry out research in order to better grasp the emotional and psychological needs of consumers for clothing. First, the image scale method was used to design the perceptual cognition experiment of the men's suit style, and the correlation analysis, factor analysis and other methods were used to reduce the multi-dimensional image to a two-dimensional space. Then, the regression analysis method was used to establish the factor analysis model of men's suit style, and the image scale distribution map of men's suit was obtained. Finally, case verification shows that the constructed model can effectively evaluate the style of men's suits. The results show that the men's suit style factors can be summarized as temperament factor and trend factor, explaining 89.741% of the style characteristics of men's suit styling distribution. Through the image scale diagram, the corresponding quadrant of each men's suit styling can be obtained, which intuitively reflects the relationship between overall styling characteristics and style imagery for men's suits. This research provides a reference for the modeling style of men's suits.

Key words: image scale, men's suit, modelling feature, style recognition, quantitative evaluation

CLC Number: 

  • TS941.12

Tab.1

Perceptual image adjective pairs"

序号 感性词对 序号 感性词对
1 正式的-休闲的 4 儒雅的-粗犷的
2 古典的-现代的 5 庄重的-活泼的
3 华贵的-朴素的 6 个性的-大众的

Fig.1

Sample figure of style of perceptual semantic questionnaire for men's suits"

Tab.2

Scoring result of style perceptual semantics"

问卷
编号
正式的-
休闲的
古典的-
现代的
华贵的-
朴素的
儒雅的-
粗犷的
庄重的-
活泼的
个性的-
大众的
1 -0.61 0.68 0.97 0.52 -0.26 1.52
2 1.00 1.13 0.58 0.32 1.03 0.87
3 0.39 0.77 0.61 0.42 0.74 0.32
4 2.71 2.61 0.9 1.23 2.52 0.06
5 1.68 1.52 1.00 0.35 1.13 0.45
6 -0.68 -0.35 -0.61 -0.58 0.10 -0.35
7 -0.29 0.19 0.42 0.03 0.13 1.39
8 1.94 1.61 0.97 1.06 1.23 0.58
9 0.16 0.48 0.45 0.52 0.42 1.06
10 -0.74 -0.48 -0.74 -0.35 -0.06 -0.87
11 1.19 0.97 -0.29 -0.16 0.68 0.45
12 2.03 1.77 0.74 0.84 0.74 0.00
13 0.42 -0.48 -1.42 -0.97 -0.13 -0.81
14 1.61 1.19 1.39 1.03 0.77 0.77
15 0.16 0.48 0.77 0.16 0.35 0.87
16 1.06 1.10 0.97 0.35 0.23 1.10
17 0.45 0.42 0.00 0.32 0.39 0.48
18 0.00 -0.03 -0.45 -0.35 0.39 -0.19
19 1.81 1.61 1.10 0.71 0.90 0.39
20 0.87 0.77 -0.06 -0.06 0.35 0.29
21 0.97 0.84 1.52 0.77 0.19 1.39
22 -0.03 0.13 -0.32 0.06 -0.16 0.16
23 0.77 1.10 0.10 0.10 0.48 0.71
24 0.10 0.77 0.26 -0.39 0.23 0.39
25 1.68 1.06 0.16 0.61 1.06 0.26
26 0.68 0.52 -1.42 -0.19 1.10 -0.55
27 1.00 0.71 0.55 0.13 0.26 0.74
28 0.58 0.42 1.23 0.71 -0.16 1.03
29 0.87 0.45 0.39 0.10 0.58 0.42
30 0.65 0.48 0.97 -0.16 0.16 1.10

Tab.3

Pearson correlation analysis results of perceptual words in men's suits"

感性词对 正式的-休闲的 古典的-现代的 华贵的-朴素的 儒雅的-粗犷的 庄重的-活泼的 个性的-大众的
正式的-休闲的 1 0.873** 0.434* 0.655** 0.782** 0.039
古典的-现代的 0.873** 1 0.606** 0.775** 0.785** 0.265
华贵的-朴素的 0.434* 0.606** 1 0.781** 0.190 0.767**
儒雅的-粗犷的 0.655** 0.775** 0.781** 1 0.532** 0.466*
庄重的-活泼的 0.782** 0.785** 0.190 0.532** 1 -0.151
个性的-大众的 0.039 0.265 0.767** 0.466* -0.151 1

Tab.4

Total variance of phonetic explanatory variables"

成分 初始特征值 提取载荷平方和
总计 方差百分比/% 累积/% 总计 方差百分比/% 累积/%
1 3.740 62.340 62.340 3.740 62.340 62.340
2 1.644 27.401 89.741 1.644 27.401 89.741
3 0.235 3.911 93.652
4 0.204 3.396 97.048
5 0.101 1.677 98.725
6 0.077 1.275 100.000

Tab.5

Rotated component matrix"

序号 感性词对 因子1 因子2
1 正式的-休闲的 0.851 -0.398
2 古典的-现代的 0.946 -0.195
3 华贵的-朴素的 0.773 0.576
4 儒雅的-粗犷的 0.905 0.166
5 庄重的-活泼的 0.719 -0.611
6 个性的-大众的 0.434 0.845

Tab.6

Component score coefficient matrix"

序号 感性词对 因子1 因子2
1 正式的-休闲的 0.228 -0.242
2 古典的-现代的 0.253 -0.118
3 华贵的-朴素的 0.207 0.350
4 儒雅的-粗犷的 0.242 0.101
5 庄重的-活泼的 0.192 -0.372
6 个性的-大众的 0.116 0.514

Fig.2

Image scale distribution"

Fig.3

Sample cases. (a)Sample a;(b)Sample b"

[1] TAKATERA M. Introduction to special issue on kansei engineering in textiles and clothing[J]. International Journal of Clothing Science and Technology, 2020,32(1):1-4.
[2] 陈伟伟. 基于感性匹配的服装协同设计原理及应用[D]. 苏州:苏州大学, 2018: 9-10.
CHEN Weiwei. The principle and application of garment collaborative design based on perceptual matching[D]. Suzhou : Soochow University, 2018: 9-10.
[3] ZHOU X X, LIANG H E, DONG Z Y. A personalized recommendation model for online apparel shopping based on kansei engineering[J]. International Journal of Clothing Science and Technology, 2017,29(1):2-13.
[4] 王海燕, 刘国联. 基于消费者感性需求的格子图案认知[J]. 纺织学报, 2014,35(11):151-156.
WANG Haiyan, LIU Guolian. Study on cognition of grid patterns based on consumer's sensibility demand[J]. Journal of Textile Research, 2014,35(11):151-156.
[5] CHEN D L, CHENG P P. The style design of professional female vest based on kansei engineering[J]. International Journal of Clothing Science and Technology, 2019,32(1):5-11.
[6] 陈丽丽, 王立川, 陈雁. 色彩感觉特性的评价[J]. 纺织学报, 2017,38(9):127-130.
CHEN Lili, WANG Lichuan, CHEN Yan. Evaluation of color perception characteristics[J]. Journal of Textile Research, 2017,38(9):127-130.
[7] 周小溪, 梁惠娥, 陈潇潇, 等. 春夏季衬衫用色织面料材质的感性评价[J]. 纺织学报, 2016,37(8):59-64.
ZHOU Xiaoxi, LIANG Hui'e, CHEN Xiaoxiao, et al. Sensibility assessment of spring and summer shirt yarn-dyed fabrics[J]. Journal of Textile Research, 2016,37(8):59-64.
[8] 张海波, 刘瑞璞, 刘莉. 男西装情感因子空间研究[J]. 东华大学学报(自然科学版), 2012,38(3):292-296.
ZHANG Haibo, LIU Ruipu, LIU Li. Study on the emotion factor space of men's suit[J]. Journal of Donghua University(Natural Science), 2012,38(3):292-296.
[9] 李淑江, 张育辉, 窦如宏. 游艇设计中感性意象空间的构建[J]. 包装工程, 2020,41(14):135-142.
LI Shujiang, ZHANG Yuhui, DOU Ruhong. The construction of perceptual image space in yacht design[J]. Packaging Engineering, 2020,41(14):135-142.
[10] 王袁笑笑生, 赵江洪, 赵丹华. 基于形面特征的汽车造型风格研究[J]. 包装工程, 2017,38(6):169-176.
WANG Yuanxiaoxiaosheng, ZHAO Jianghong, ZHAO Danhua. Automobile design style based on styling surface features[J]. Packaging Engineering, 2017,38(6):169-176.
[11] 黄黎清, 何灿群, 方方, 等. 基于意象尺度图的重型卡车造型设计[J]. 包装工程, 2016,37(24):21-26.
HUANG Liqing, HE Canqun, FANG Fang, et al. Form design of heavy truck based on image scale[J]. Packaging Engineering, 2016,37(24):21-26.
[12] 邵丹, 朱莉思. 基于眼动实验的服装品牌风格意象认知探析:E品牌上装风格案例研究[J]. 东华大学学报(自然科学版), 2013,39(2):240-246.
SHAO Dan, ZHU Lisi. Research on image cognitive of branded apparel based on eye-tracking technology: case study of E brand and product style analysis[J]. Journal of Donghua University(Natural Science), 2013,39(2):240-246.
[13] 黄帅, 张毅, 周志华. 采用因子分析法的服用织物电磁屏蔽性能影响因素分析[J]. 纺织学报, 2016,37(2):149-154.
HUANG Shuai, ZHANG Yi, ZHOU Zhihua. Analysis of influencing factors of clothing fabric on electromagnetic shielding performance based on factor analysis[J]. Journal of Textile Research, 2016,37(2):149-154.
[1] XIA Ming, SONG Jing, JIANG Zhaoyang, MA Yanbin. Style recognition technique based on feature matching in dress construction [J]. Journal of Textile Research, 2020, 41(07): 141-146.
[2] . Clothing style recognition approach using Fourier descriptors and support vector machines [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(05): 122-127.
[3] WU Zhiming;LI Yi. Clothing fashion color image scale grey forecast [J]. JOURNAL OF TEXTILE RESEARCH, 2009, 30(04): 94-100.
[4] LIU Gui;YU Weidong. Quantitative evaluation method for the significance of worsted fore-spinning parameters based on BP neural network [J]. JOURNAL OF TEXTILE RESEARCH, 2008, 29(1): 34-37.
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