An image-based shape analysis approach and its application to young women’s waist-hip-leg position

Abstract

Digital image processing has been widely used for researches in the fashion industry. This study presented a method to classify women’s waist-hip-leg position based on body images. 135 healthy female students were selected as the experimental subjects, and then photo and 3D body measuring methods were used to obtain 40 shape parameters. Through factor analysis, five factors were extracted for the waist-hip position and eight factors for the leg position. The waist-hip and leg were separately classified into four categories after clustering analysis with optimised factors. The distribution of waist-hip-leg shape was analysed by combining the waist-hip and leg classification results. The results showed that 12.31% of the subjects had prominent abdomens, flat buttocks, and round and thin legs. The landmarks and parameters were automatically extracted for waist-hip-leg shape identification. The image-based shape analysis approach (ISA) was finally verified with an accuracy rate of over 90% with 30 new subjects.

Practitioner summary: This study will propose an image-based shape analysis approach (ISA) to realise the quick automatic shape identification of the waist-hip-leg position based on body images. In addition, the method can be applied to pants’ pattern alteration for different body types by analysing the relationship between the waist-hip-leg shape and pants.

Keywords:

• Waist-hip-leg
• body image
• shape parameter
• automatic recognition
• pattern alteration

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Funding

This paper was supported by the National Natural Science Foundation of China [Grant No. 61702461], Application and Basic Research Project of China National Textile and Apparel Council [Grant No. J202007], the Fundamental Research Funds of Zhejiang Sci-Tech University [Grant No. 2020Q051], the Clothing Culture Innovation Team of Zhejiang Sci-Tech University [Grant No. 11310031282006], and Science and Technology Guiding Project of China National Textile and Apparel Council [Grant No. 2018079].