Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (11): 129-136.doi: 10.13475/j.fzxb.20201207808

• Apparel Engineering • Previous Articles     Next Articles

Style similarity algorithm based on clothing style

JIANG Hui, MA Biao()   

  1. Glorious Sun School of Business and Management, Donghua University, Shanghai 200051, China
  • Received:2020-12-29 Revised:2021-08-01 Online:2021-11-15 Published:2021-11-29
  • Contact: MA Biao E-mail:mabiao@dhu.edu.cn

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Abstract:

In order to calculate accurately the similarity between clothing images so as to meet the cross-scene needs of more users searching for clothing matching images for purchasing similar clothing, the influencing factors of clothing styles were investigated based on clothing styles constructed according to quantitative standards. The feature model of styles was established to further analyze the shortcomings of existing clothing attribute recognition algorithms, and was used to identify styles based on deep learning. The features of clothing styles were depicted through constructing a residual network model that integrates transfer learning. The experimental results show that the precision of the model on clothing style features is close to 90%, and the overall accuracy reaches 80%. Compared with the traditional image similarity methods, the accuracy of the image similarity calculation based on clothing styler is higher.This research also provides new ideas for personalized clothing recommendation.

Key words: clothing style, image similarity, deep learning, style characteristic, transfer learning, residual neural network

CLC Number: 

  • TP391.41

Fig.1

Image similarity example. (a)Clothing image 1; (b)Clothing image 2; (c) Natural image 1;(d) Natural image 2"

Fig.2

Model for calculating style similarity based on clothing style"

Fig.3

Clothing image style feature recognition model (residual network model)"

Fig.4

Residual block structure diagram"

Fig.5

Improvement of residual block-bottleneck"

Tab.1

ResNet50's network structure"

网络层名称 输出大小/个 ResNet50网络结构
卷积层1 112×112 7×7, 64, 步长2
卷积层2 56×56 1 × 1 64 3 × 3 64 1 × 1 256 ×3
卷积层3 28×28 1 × 1 128 3 × 3 128 1 × 1 512 ×4
卷积层4 14×14 1 × 1 256 3 × 3 256 1 × 1 1024 ×6
卷积层5 7×7 1 × 1 512 3 × 3 512 1 × 1 2048 ×3
全连接层 1×1 平均池化,指数函数softmax

Fig.6

Flow chart of recognition algorithm of clothing image style features"

Tab.2

Experimental results"

属性类别 训练集或
测试集		 准确率 平均精度 交叉熵
损失率
Collar_design 训练集 0.876 0.931 0.350
测试集 0.805 0.890 0.533
Lapel_design 训练集 0.900 0.945 0.277
测试集 0.844 0.910 0.494
Neck_design 训练集 0.835 0.907 0.458
测试集 0.728 0.843 0.700
Neck_line 训练集 0.831 0.902 0.484
测试集 0.808 0.886 0.594
Skirt_length 训练集 0.823 0.900 0.487
测试集 0.768 0.867 0.646
Pant_length 训练集 0.874 0.932 0.355
测试集 0.810 0.897 0.533
Coat_length 训练集 0.793 0.882 0.592
测试集 0.770 0.870 0.652
Sleeve_length 训练集 0.841 0.912 0.449
测试集 0.834 0.909 0.456

Fig.7

Examples of clothing images. (a)Lady dress 1; (b)Lady dress 2; (c) Bohemian dress;(d) National dress"

Tab.3

Compare clothing similarity based on style"

对比图像 相似度计算方法 相似度
图7(a)、(b) 基于本文的款式风格特征 0.933
传统的图像余弦相似度 0.881
图7(c)、(d) 基于本文的款式风格特征 0.684
传统的图像余弦相似度 0.906
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