Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (03): 89-94.doi: 10.13475/j.fzxb.20210310406

• Textile Engineering • Previous Articles     Next Articles

Fast image retrieval of colored spun fabrics on high-level semantic features

GU Qian1,2, YUAN Li1,2(), YANG Yali1, LIU Junping3   

  1. 1. School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan, Hubei 430200, China
    2. Hubei Functional Fiber Processing and Testing Engineering Technology Research Center, Wuhan Textile University,Wuhan, Hubei 430200, China
    3. School of Mathematics and Computer, Wuhan Textile University,Wuhan, Hubei 430200, China
  • Received:2021-03-20 Revised:2021-11-21 Online:2022-03-15 Published:2022-03-29
  • Contact: YUAN Li E-mail:yuanli@wtu.edu.cn

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Abstract:

In order to improve the effectiveness and execution efficiency of image retrieval systems for colored spun fabrics, an image retrieval system based on low-order features and high-level semantic features was established. The system uses the deep convolution neural network to extract the style semantic features of colored spun fabrics, and then constructs a hierarchical retrieval system by fusing low-order texture features such as local binary pattern and directional gradient histogram. Binary hash coding was used to reduce the dimension of the extracted high-dimensional feature vector. The results showed that for the retrieval of the 9 sample images of colored spun fabrics with different texture styles, the Top-10 recall and average accuracy of the system reached 97.37% and 87.54%, respectively, indicating good effectiveness and robustness. Compared with the direct use of advanced semantic features, the retrieval time of the proposed method is about 750 times faster.

Key words: colored spun fabric, image retrieval, high-level semantic feature, binary hash code, hierarchical characteristic

CLC Number: 

  • TS101.9

Fig.1

Low order characteristics of colored spun fabrics. (a) Original image of fabric; (b) LBP pseudo gray spectrum; (c) HOG feature map"

Fig.2

Advanced semantic feature extraction"

Tab.1

Sample parameter"

样本名称 线密度/tex 面密度/(g·m-2) 样本数量
春芽纱 22.5 170 56
春芽竹节纱 18.2 160 170
幻影纱 19.5 160 64
迷你幻影纱 22.5 170 64
霓彩纱 19.5 160 60
赛络色纺纱 19.5 160 40
手纺纱 29.2 175 160
水蚊蝇带纱 36.5 190 10
星彩纱 18.2 140 60

Fig.3

Sample drawing of colored spun fabrics. (a)Budding heather yarn;(b)Home spun heather yarn;(c)Mirage mini heather yarn;(d)Siro heather yarn"

Tab.2

Hash codes length and Top-10 search results"

哈希码长度 查全率/% 平均准确率/%
16 90.37 46.58
32 91.46 48.12
64 94.52 49.36
128 96.03 50.88
256 98.87 50.97

Tab.3

LBP operator parameters and Top-10 search results"

LBP算子参数 查全率/% 平均准确率/%
P R
8 1 94.68 81.62
8 2 97.04 83.77
16 1 96.41 79.34
16 2 96.67 77.57

Tab.4

HOG operator parameters and Top-10 search results"

HOG算子参数 查全率/% 平均准确率/%
c b
36 4 94.03 82.77
36 9 93.85 80.68
64 4 96.57 83.32
64 9 95.76 82.95

Tab.5

Search experimental results"

样本名称 查全率/% 平均准确率/%
春芽纱 97.55 87.32
春芽竹节纱 98.21 89.28
幻影纱 97.56 87.78
迷你幻影纱 97.85 88.14
霓彩纱 97.63 87.03
赛络色纺纱 96.11 86.95
手纺纱 98.37 89.47
水蚊蝇带纱 95.21 85.61
星彩纱 97.84 86.28

Fig.4

Retrieval results of similar colored spun fabrics. (a) Retrieval image; (b) Similarity Top-10 result image"

Fig.5

Colored spun fabric wrinkle image"

Fig.6

Retrieval results of abnormal colored spun fabrics. (a) Retrieval image; (b) Similarity Top-10 result image"

Tab.6

Contrast experiment Ⅰ results"

实验方法 查全率/% 平均准确率/%
方法1 67.54 53.83
方法2 85.62 61.43
方法3 78.65 66.57
方法4 84.22 80.33
本文方法 97.37 87.54
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