Journal of Textile Research ›› 2025, Vol. 46 ›› Issue (06): 135-142.doi: 10.13475/j.fzxb.20240600501

• Textile Engineering • Previous Articles     Next Articles

Research on content-based interactive fabric image recommendation

MING Yuhao, ZHANG Ning, XIANG Jun, PAN Ruru()   

  1. College of Textile Science and Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2024-06-03 Revised:2024-09-27 Online:2025-06-15 Published:2025-07-02
  • Contact: PAN Ruru E-mail:prrsw@163.com

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Abstract:

Objective With rapid development of textile industry, the types of fabrics are increasingly diversified. Facing a huge fabric library, it is difficult for the traditional retrieval method to carry out accurate search particularly when the user needs are not defined clearly enough. The popularity of e-commerce platforms has promoted the application of recommendation algorithms. The main idea of recommendation algorithms is to proactively recommend relevant content that users may be interested in by analyzing their preferences. Therefore, this paper proposes a content-based interactive fabric image recommendation algorithm, which considers the color and texture characteristics of the fabric based on the user's interest, aiming to accurately capture the user's preference, recommend the fabric for the user's needs, and provide certain reference value for the research of personalized fabric recommendation algorithms.

Method This research considers the visual features such as fabric color and texture, and links the user's preferences with the characteristics of fabric, and proposes a content-based interactive fabric image recommendation algorithm. First, the main color set of the image is extracted from HSV color space, and the fabric texture is classified using the transfer learning algorithm based on ResNet18 network model. On this basis, an interactive feature preference recommendation model is established. The model calculates the interest value of each feature attribute according to the user's preference score for the fabric. Used to predict user ratings for potential fabrics. The 9 fabrics with the highest rating are recommended to the user, and the performance of the recommendation model is evaluated according to the user's rating for the recommendation results.

Results The obtained data set was divided into training set, verification set and test set according to the ratio of 8∶1∶1. In this paper, the transfer learning algorithm based on the ResNet18 network model was used to classify fabric textures, and the highest accuracy of the test set was 0.936. It is shown that the ResNet18 network model has a good performance for fabric image texture classification. According to the rating preferences of 30 testers for the 9 recommended fabrics, the average score of these 30 users is above 5.5 points, in line with the range of 4-7 points (generally interested), and most of the scores gather in the range of 7-10 points (very interested), among which 19 people have an average score of more than 8 points, and 4 people have an average score of more than 9 points. The percentage of users who are very interested in the recommendation results as a whole is as high as 93.3%, among which 18 users score at least 7 of the 9 recommended fabrics above 8 points, which indicates that the overall performance of the recommendation model proposed in this paper is good.

Conclusion In view of the fact that traditional recommendation algorithms fail to consider the influence of visual features of fabric on users' interest in fabric recommendation, this paper classifies fabric color and texture based on hand-extracted features and higher-order features respectively. Experimental results show that the transfer learning algorithm based on ResNet18 network model has achieved good results in fabric texture classification. The highest accuracy of the test set reached 0.936. Subsequent experiments will continuously optimize the accuracy and reduce the impact of classification errors on the recommendation results. In this paper, an interactive feature preference recommendation model is established by means of interactive scoring. The model calculates the interest value of each feature attribute according to the user's preference rating, so as to predict the user's rating on potential fabrics, and then recommend fabric images with high predictive ratings for users. Users' preferences for the recommendation results can be used as a standard to evaluate the performance of the recommendation model. The experimental results show that most users are satisfied with the recommendation results, which indicates that the overall performance of the recommendation model proposed in this paper is good, and it can capture users' interests and preferences more accurately, providing certain reference value for fabric image recommendation.

Key words: fabric design, HSV color space, fabric image recommendation algorithm, fabric feature, visual feature, user preference

CLC Number: 

  • TS941.26

Fig.1

Framework of content-based interactive fabric image recommendation"

Fig.2

Fabric recommendation interface"

Tab.1

Fabric feature matrix"

色系 h s v
黑色 [0, 360] [0, 1] [0, 0.2]
灰色 [0, 360] [0, 0.2] [0.2, 0.8]
白色 [0, 360] [0, 0.2] [0.8, 1]
红色 (315, 360]∪[0, 20] [0.2, 1] [0.2, 1]
黄色 (20, 45] [0.2, 1] [0.2, 1]
绿色 (45, 90] [0.2, 1] [0.2, 1]
青色 (90, 160] [0.2, 1] [0.2, 1]
蓝色 (160, 270] [0.2, 1] [0.2, 1]
紫色 (270, 315] [0.2, 1] [0.2, 1]

Fig.3

Residual network structure diagram"

Fig.4

Plot of training set loss and test set accuracy"

Tab.2

Fabric feature matrix"

面料序号 C1, C2,…, C9 T1, T2,…, T6
i1 0,1,…,0 1,0,…,0
i2 1,0,…,1 0,1,…,0
︙ ︙ ︙
ik 1,1,…,0 0,0,…,0

Tab.3

User-feature interest matrix"

用户序号 C1, C2,…, C9 T1, T2,…, T6
u1 7.53, 3.61,…, 5.87 2.83, 9.52,…, 7.30
u2 2.33, 8.56,…, 0 0, 6.58,…, 8.23
︙ ︙ ︙
un 1.30, 2.71,…, 7.82 3.55, 2.01,…, 9.34

Fig.5

Six fabric textures.(a) Solid; (b) Lattice; (c) Stripe; (d) Plinth; (e) Puncture point; (f) Brid eye"

Tab.4

Data set details"

数据集
编号		 图像数量/张
训练集 验证集 测试集
Q1 736 92 92
Q2 624 78 78
Q3 496 62 62
Q4 672 84 84
Q5 640 80 80
Q6 328 41 41

Fig.6

Values of user's interest in feature attribute"

Fig.7

Fabric recommendation results for user u10 (a) and user u19 (b)"

Tab.5

Average rating and highly rated fabric sheets of 30 users"

用户
编号		 平均
评分		 高评分
面料数		 用户
编号		 平均
评分		 高评分
面料数
u1 8.34 7 u16 8.66 8
u2 8.13 6 u17 7.55 6
u3 7.38 6 u18 5.62 3
u4 8.00 7 u19 7.89 6
u5 9.50 9 u20 8.51 7
u6 7.44 6 u21 7.21 4
u7 7.60 7 u22 8.11 5
u8 7.35 7 u23 9.13 8
u9 6.37 3 u24 8.80 8
u10 8.67 9 u25 7.26 5
u11 8.10 7 u26 8.50 7
u12 8.73 7 u27 8.36 7
u13 7.38 7 u28 8.92 8
u14 8.73 6 u29 8.15 6
u15 9.80 9 u30 9.42 9
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