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"

[1] 唐婕. 基于iOS平台的智能衣柜管理APP的设计与实现[D]. 北京:北京邮电大学, 2017:5-7.
TANG Jie. Design and implementation of an intelligent clothing management APP based on iOS platform[D]. Beijing:Beijing University of Posts and Telecommunications, 2017:5-7.
[2] 张泽堃, 张海波. 服装智能搭配研究现状综述[J]. 网络安全技术与应用, 2019(10):128-130.
ZHANG Zekun, ZHANG Haibo. A summary of the research status of intelligent clothing collocation[J]. Network Security Technology and Application, 2019(10):128-130.
[3] 曹纯, 仲兆满. 智能衣物搭配系统的设计与实现[J]. 福建电脑, 2019, 35(7):21-25.
CAO Chun, ZHONG Zhaoman. Design and implementation of intelligent clothing matching system[J]. Journal of Fujian Computer, 2019, 35(7):21-25.
[4] 梁俊杰, 韦舰晶, 蒋正锋. 生成对抗网络GAN综述[J]. 计算机科学与探索, 2020, 14(1):1-17.
LIANG Junjie, WEI Jianjing, JIANG Zhengfeng. Overview of GAN generation adversarial network[J]. Computer Science and Exploration, 2020, 14(1):1-17.
[5] 邹秀芳, 朱定局. 生成对抗网络研究综述[J]. 计算机系统应用, 2019, 28(11):1-9.
ZHOU Xiufang, ZHU Dingju. Review on generative adversarial network[J]. Computer Systems & Applications, 2019, 28(11):1-9.
[6] ISOLA Phillip, ZHU Junyan, ZHOU Tinghui, et al. Image-to-image translation with conditional adversarial networks[C] // 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). New Jersey: Institute of Electrical and Electronic Engineers, 2017:5967-5976.
[7] BARDIS Michelle. Deep learning with limited data: organ segmentation performance by U-Net[J]. Electronics, 2020, 9(8) :1199.
doi: 10.3390/electronics9081199
[8] DONG C, LOY C C, HE K, et al. Image Super-Resolution Using Deep Convolutional Networks[C]// IEEE Transactions on Pattern Analysis and Machine Intelligence. New Jersey: Institute of Electrical and Electronic Engineers, 2016: 295-307.
[9] GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[C] // NIPS'17 Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates, 2017:5769-5779.
[10] 刘方园, 王水花, 张煜东. 方向梯度直方图综述[J]. 计算机工程与应用, 2017, 53(19):1-7,25.
LIU Fangyuan, WANG Shuihua, ZHANG Yudong. Survey on histogram-based optimization[J]. Computer Engineering and Applications, 2017, 53(19):1-7,25.
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