Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (07): 164-168.doi: 10.13475/j.fzxb.20200803505

• Apparel Engineering • Previous Articles     Next Articles

Recommendations for user's bottoms matching based on generative adversarial networks

YANG Zhengyan1, XUE Wenliang1(), ZHANG Chuanxiong2, DING Yi1, MA Yanxue1   

  1. 1. College of Textiles, Donghua University, Shanghai 201620, China
    2. Science and Technology Development Center of Textile Industry, Beijing 100020, China
  • Received:2020-08-05 Revised:2021-04-22 Online:2021-07-15 Published:2021-07-22
  • Contact: XUE Wenliang E-mail:xwl@dhu.edu.cn

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Abstract:

In order to avoid repetitive purchases of similar clothes and to solve the clothing matching problem, this research worked on an intelligent clothing matching system for providing recommendations to customers and end-users. A crawler technology was used to obtain a large number of clothing matching data from mid-to-high end brands, and the model of deep learning-generative adversarial network (GAN) was adopted to learn clothing data to explore the visual perceptions of colors, styles, and so on. When inputting a piece of top clothing in the system, a bottom picture can be generated intelligently and then matched to the user's personal wardrobe together with the consideration of the surrounding temperature. The effectiveness of this method was verified by comparing the original matching with the model generated matching.This work provides new ideas for intelligent clothing matching.

Key words: deep learning, generative adversarial networks, intelligent decision on clothing match, clothing match

CLC Number: 

  • TS941.13

Fig.1

System framework"

Fig.2

Schematic diagram of GAN model"

Fig.3

Effect of model training. (a) Real top image;(b) Real bottom image; (c) Generate bottom image after 5 epoch training;(d) Generate bottom image after 800 epoch training"

Fig.4

Effect of model testing. (a) Real top image;(b) Real bottom image; (c) Generate bottom image after 5 epoch training;(d) Generate bottom image after 800 epoch training"

Fig.5

Recommendation process"

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