A novel evaluation method of Chinese female lower body shapes based on machine learning

IF 1 4区 工程技术 Q3 MATERIALS SCIENCE, TEXTILES
Xiaofeng Yao, Jinzhu Shen, Jianping Wang
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引用次数: 0

Abstract

Purpose

The purpose of this paper is to define the evaluation criteria for Chinese female lower body shapes and simplify the evaluation process of shapewear, including girdles, shaping pants, etc.

Design/methodology/approach

The study utilized a machine learning algorithm based on support vector regression and optimized by a genetic algorithm to construct an evaluation model for the contour beauty of Chinese female lower body shapes. A total of 64 virtual 3D models of women were measured. These models were rated by 42 raters using the Likert 9 psychological scale and data was obtained from 310 female samples.

Findings

Eight factors were selected and used to create a regression prediction model. The model achieved an accuracy of 84.7% for the training samples and 86.6% for the test samples.

Originality/value

The model can be utilized to assess the aesthetic appeal of the female lower body and to evaluate the shaping impact of shapewear. The research and evaluation of shapewear for the female lower body are of great significance, particularly in enhancing production efficiency.

基于机器学习的中国女性下半身体型评价新方法
本文旨在定义中国女性下半身体形的评价标准,简化塑身衣(包括束腰、塑身裤等)的评价过程。研究利用基于支持向量回归的机器学习算法,并通过遗传算法进行优化,构建了中国女性下半身体形轮廓美的评价模型。共测量了 64 个虚拟女性三维模型。这些模型由 42 名评分者使用李克特 9 级心理量表进行评分,数据来自 310 个女性样本。该模型在训练样本中的准确率为 84.7%,在测试样本中的准确率为 86.6%。原创性/价值该模型可用于评估女性下半身的美感和塑身衣的塑形效果。对女性下半身塑身衣的研究和评估具有重要意义,尤其是在提高生产效率方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.40
自引率
8.30%
发文量
51
审稿时长
10 months
期刊介绍: Addresses all aspects of the science and technology of clothing-objective measurement techniques, control of fibre and fabric, CAD systems, product testing, sewing, weaving and knitting, inspection systems, drape and finishing, etc. Academic and industrial research findings are published after a stringent review has taken place.
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