Discriminating the fit of women's suits based on 2D human body image feature learning
Garment fit evaluation is one of the main bottlenecks in current fashion design and manufacturing.In the process of remote customization and online shopping,the size of the clothes that does not fit has become the primary reason for users to return or exchange them,which has seriously affected the intelligent upgrade of the clothing industry.Therefore,how to judge the fit degree of a garment without actually trying it on is one of the main problems that needs to be solved urgently in the clothing industry.In previous studies,garment fit was usually evaluated by some characteristic indexes,such as ease allowance,clothing wrinkle,the spatial relationship between clothing and the human body,and clothing pressure.However,most of them have some problems such as the need to try on clothes and the complicated evaluation process,which are not suitable for remote customization of clothing and online shopping.With the development of computer vision and machine learning technology,a small number of scholars use 2D images to judge garment fit based on clothing wrinkle,but only for specific garments,and the generalization ability is weak.To solve the problem that garment fit cannot be discriminated in advance in the process of remote customization and online shopping,this study took women's suits as an example and put forward a method to discriminate the fitness of women's suits based on 2D human images.Firstly,2D images and 3D human body size of 462 young women aged 18-25 in northeast China were collected,and a human body data set was established.Twenty-two samples were selected from this data set,and virtual models were established for virtual try-on by using CLO3D software.According to the national clothing size standard of China,10 virtual women's suits with the same style and different sizes were made to establish the garment data set.Based on the principle of Kansei engineering,the fitting evaluation labels of women's suits,namely tight,fit and loose types were given by expert evaluation method combined with virtual fitting.Then,a method to discriminate the fit of women's suits by using garment features and 2D human image features was proposed.As for the garment features(GFs),the bust girth and length characteristics of women's suits,the features of 2D human body image including the key point distance features(DFs)and overall features(OFs)were extracted.The specific extraction method is as follows.Firstly,the 2D human body image was preprocessed and normalized,and the human body contour was extracted by Canny edge detector,and the five DFs in the 2D human body image were obtained,namely the distance of chest thickness key points distance,waist thickness key points distance,hip thickness key points distance,waist width key points distance and hip breadth key points distance.Then,three OFs of the 2D human body image were extracted,namely,the body height pixel value feature(H),the feature of the projected unit area(p),and the feature of the projected area ratio of the front and side of the human body(C).Finally,the Bayesian classifier was used to establish the garment fit prediction model,and the Fisher linear discriminant function was used to establish the women's suit fit discriminant equation.In this paper,computer vision technology was used to extract the feature indexes of garment fit evaluation,and a machine learning algorithm was combined to realize the women's suit fit discrimination based on 2D human body images.Experimental results indicate that,the garment features proposed in this paper and the 2D human body image features based on computer vision technology can be used to predict the fit of women's suits,and the discriminant model of women's suits based on the Bayesian algorithm has achieved good discriminant accuracy,and the cross-validation discriminant accuracy rate can reach 84.8％,so the model is valid.The women's suit fit discrimination model based on 2D human image feature learning provides an effective and feasible method for garment fit in fashion design,production,manufacturing,and online shopping.This method provides a theoretical basis for the quantitative evaluation of garment fit and is beneficial to the recommendation of clothing size and the improvement of patterns in clothing customization enterprises and online shopping.
2D human body images featurecomputer visionmachine learningwomen's suitgarment fitBayes model
万方数据
维普
