TY  - JOUR
TI  - Decoding the fashion trend of sports shoes with empowered computer vision
AU  - Lu, Qian
AU  - Li, Jingjing
AU  - Lin, Ziseng
AU  - Zhou, Jin
T2  - Industria Textila
AB  - To adapt to the rapidly changing market and capricious trends, fashion brands need to understand trends and market
conditions precisely and fast to produce marketable products. The traditional fashion trend analysis has relied heavily
on the subjective judgement of experts, inevitably leading to biased decisions and is time-consuming. The development
of computer vision and machine learning provides an objective and systematic approach to processing images and
analysis of fashion products. However, most studies focus on clothing trends analysis, few are on shoe trends analysis.
Hence, this study aimed to decode and analyse the fashion trend of sports shoes with empowered computer vision
technology. In this paper, a dataset containing e-commerce images of sports shoes with precise annotations was
established; then Mask-RCNN was utilized to classify and extract the shoe from the background image; a modified
version of the K-means clustering algorithm was employed to detect the shoe colour. The results indicated that
fashionable sports shoes and casual sports shoes were the most prevalent two styles. Besides neutral tones, yellow,
red ochre and reddish orange were popular in casual sports shoes, fashion sports shoes and basketball shoes
respectively and Atlantic Blue in board shoes and trainers. This study demonstrated the promising potential of computer
vision and machine learning as a new method to analyse footwear fashion trends efficiently and economically.
DA  - 2023/10/31/
PY  - 2023
DO  - 10.35530/IT.074.05.2022107
DP  - DOI.org (Crossref)
VL  - 74
IS  - 05
SP  - 519
EP  - 526
SN  - 12225347
UR  - http://revistaindustriatextila.ro/images/2023/5/003%20QIAN%20LU%20INDUSTRIA%20TEXTILA%20no.5_2023.pdf
Y2  - 2023/11/05/19:14:37
ER  -