Journal of Textile Research ›› 2023, Vol. 44 ›› Issue (08): 110-117.doi: 10.13475/j.fzxb.20220400301

• Textile Engineering • Previous Articles     Next Articles

Research on fabric pattern retrieval based on SURF and VLAD feature coding

ZHAO Wenhao, XIANG Jun, ZHANG Ning, PAN Ruru()   

  1. College of Textile Science and Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • Received:2022-04-01 Revised:2023-05-06 Online:2023-08-15 Published:2023-09-21

RichHTML

3

PDF (PC)

29

Abstract:

Objective With the increasing proportion of patterned fabrics produced in fabric manufacturing enterprises, there have been more requirements relating to the production and management of such fabrics, including querying the production materials of imitation, reducing the inventory management costs. The current textile pattern retrieval algorithm has low retrieval efficiency and poor adaptability, which is not conducive to the retrieval of fabric patterns by fabric enterprises. Therefore, a fast and accurate retrieval algorithm for similar patterns was proposed to help enterprises improve the utilization rate of production materials and reduce the cost of fabric inventory management, and promote textile fabric enterprises to enhance the competitiveness of the industry.

Method This research proposed a textile pattern retrieval algorithm based on SURF (speeded up robust features) and VLAD (vector of locally aggregated descriptors) feature coding. A fabric database with patterns was constructed, and SURF features of the image were extracted to express the image content, before the original fabric was clustered to generate a visual dictionary. The generated visual dictionary encoded the SURF features of the fabric image in the database with VLAD features to aggregate to generate VLAD vectors. On the premise of ensuring the ability of VLAD to express images, principal component analysis was performed on the generated VLAD vectors to reduce the vector dimensions and improve the retrieval efficiency. Finally, the Ball tree algorithm was used to build the index to accelerate the matching speed and improve the retrieval efficiency.

Results In order to obtain good retrieval performance, the parameters of the algorithm used were optimized. Through the comparison of retrieval precision and time, it was seen that within a certain range the precision of fabrics was improved with the increase of dictionary size K. However, when K was increased to 1 024, the precision only increased slightly with the retrieval time doubled. As the size of the visual dictionary was increased, the complexity and time cost of training the visual dictionary were greatly increased. Considering comprehensively, the dictionary size K=512 was taken in this research. From the perspectives of the retrieval precision and time, it was seen that with the reduction of feature dimensions, the precision of fabrics was constantly decreasing. When the dimension was reduced to 64, the precision of fabrics decreased greatly, with a slow reduction in retrieval time. Considering the effectiveness and time complexity of the algorithm, the feature dimension D=512 was selected in the subsequent experiments of this research. In order to highlight the advantages of the algorithm in this paper, this paper compared the "ORB (Oriented FAST and Rotated BRIEF)+VLAD", "HOG (Histogram of Oriented Gradient)+VLAD", "Color Moments (CM)", "Color Moments+ Gray-level Co-occurrence Matrix" and "Color Histogram+ Gray-level Co-occurrence Matrix" for retrieval verification. The average retrieval accuracy of the algorithm reached 83.5%, the average retrieval time was 0.488s, and the comprehensive performance was better than other algorithms. From the P-R curves, it was evident that the algorithm developed in this research performed the best on the data set built. "HOG+VLAD" was slightly better than "ORB+VLAD", indicating that SURF was able to better describe the characteristics of patterned fabrics than ORB and HOG.

Conclusion The experimental results show that when the scale of visual dictionary K is 512 and the number of reserved dimensions is 512, the average retrieval accuracy of the algorithm for fabric pattern retrieval is 83.5%, the average retrieval time is 0.488 seconds, and the algorithm has good size invariance and rotation invariance. It has good performance and adaptability for fabric pattern retrieval, which has certain practical significance for the practical application of textile enterprises.

Key words: fabric retrieval, SURF feature, VLAD feature coding, fabric pattern, principal component analysis

CLC Number: 

  • TS941.26

Fig. 1

Construction of scale space and location of feature points"

Fig. 2

Generation of feature descriptor"

Fig. 3

Generation of codebook"

Fig. 4

Generation of VLAD"

Fig. 5

Spherical tree structure of data set"

Fig. 6

Flow chart of pattern fabric retrieval algorithm"

Tab. 1

Comparison of retrieval precision and time under different visual dictionary sizes"

词典规模(K) 查准率/% 平均检索时间/s
32 86.3 0.382
64 90.5 0.401
128 93.8 0.413
256 94.3 0.434
512 95.5 0.488
1 024 95.9 0.862

Tab. 2

Influence of dimension on retrieval precision and time"

维度(D) 查准率/% 平均检索时间/s
1 024 95.8 0.753
512 95.5 0.488
256 92.9 0.407
128 88.3 0.378
64 81.6 0.369

Fig. 7

Display of retrieval effect"

Tab. 3

Comparison of mean average precision and retrieval time of different algorithms"

算法 平均检索精度/% 检索时间/s
本文算法 83.5 0.488
ORB+VLAD 66.8 0.358
HOG+VLAD 70.2 0.523
CM 40.3 0.263
文献[14]方法 56.7 0.627
文献[15]方法 63.8 0.755

Fig. 8

Retrieval effect of different algorithm"

Fig. 9

P-R curves of different algorithms"

[1] SZTANDERA L M, CARDELLO A V, WINTERHALTER C, et al. Identification of the most signifycant comfort factors for textiles from processing mechanical, handfeel, fabric construction, and perceived tactile comfort data[J]. Textile Research Journal, 2012, 83(1): 34-43.
doi: 10.1177/0040517512438121
[2] XIANG J, ZHANG N, PAN R, et al. Fabric image retrieval system using hierarchical search based on deep convolutional neural network[J]. IEEE Access, 2019, 7: 35405-35417.
doi: 10.1109/Access.6287639
[3] 张宁. 基于特征工程的机织面料图像检索方法研究[D]. 无锡: 江南大学, 2021:1-50.
ZHANG Ning. Research on image retrieval method of woven fabric based on Feature Engineering[D]. Wuxi: Jiangnan University, 2021:1-50.
[4] 康锋, 胡洁, 张华熊, 等. 基于最大稳定极值区域的织物图案检索[J]. 纺织学报, 2015, 36(10): 156-160.
KANG Feng, HU Jie, ZHANG Huaxiong, et al. Fabric pattern retrieval based on maximum stable extremum region[J]. Journal of Textile Research, 2015, 36(10): 156-160.
[5] 向忠, 何旋, 钱淼, 等. 基于边缘和颜色特征的织物印花花型检索[J]. 纺织学报, 2018, 39(5): 137-143.
XIANG Zhong, HE Xuan, QIAN Miao, et al. Fabric printing pattern retrieval based on edge and color features[J]. Journal of Textile Research, 2018, 39(5): 137-143.
[6] 曹霞, 李岳阳, 罗海驰, 等. 蕾丝花边的改进型纹理特征检索方法[J]. 纺织学报, 2016, 37(6): 142-147,154.
CAO Xia, LI Yueyang, LUO Haichi, et al. Improved texture feature retrieval method for lace[J]. Journal of Textile Research, 2016, 37(6): 142-147,154.
[7] 刘颖, 倪天宇, 王富平, 等. 融合局部聚合描述符和全局特征的现勘图像分类算法[J]. 科学技术与工程, 2020, 20(3): 1118-1124.
LIU Ying, NI Tianyu, WANG Fuping, et al. An image classification algorithm based on local aggregation descriptors and global features[J]. Science Technology and Engineering, 2020, 20(3): 1118-1124.
[8] 江南大学. 基于SURF和VLAD特征编码的面料图案检索算法: CN202210889749. 0[P]. 2022-11-01.
Jiangnan University. Fabric pattern retrieval algorithm based on SURF and VLAD feature coding: CN202210889749. 0[P]. 2022-11-01.
[9] BAY H, TUYTELAARS T, LUC Van Gool. SURF: speeded up robust features[J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.
doi: 10.1016/j.cviu.2007.09.014
[10] 修瑛昌, 杨文静. Mini Batch K-means算法在遥感影像分类中的应用[J]. 鲁东大学学报(自然科学版), 2017, 33(4): 359-363.
XIU Yingcang, YANG Wenjing. Application of mini batch k-means algorithm in remote sensing image classification[J]. Journal of Ludong University, 2017, 33(4): 359-363.
[11] JÉGOU H, DOUZE M, SCHMID C, et al. Aggregating local descriptors into a compact image representa-tion[C]// 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CA:IEEE. 2010: 3304-3311.
[12] RETONDARO L C d S C, ESPERANÇA C. Optimized 2D Ball Trees[J]. Computer Science, 2021. 10.1109/sibgrapi54419.2021.00014.
[13] 房梦玉, 马明栋. 改进的PCA-LDA人脸识别算法的研究[J]. 计算机技术与发展, 2021, 31(2): 65-69.
FANG Mengyu, MA Mingdong. Research on improved PCA-LDA face recognition algorithm[J]. Computer technology and development, 2021, 31 (2): 65-69.
[14] AHSANI A F, SARI Y A, ADIKARA P P. Food image retrieval with gray level co-occurrence matrix texture feature and CIE L*a*b* color moments feature[C]//2019 International Conference on Sustainable Information Engineering and Technology (SIET). Malang:IEEE, 2019: 130-134.
[15] YUE J, LI Z, LIU L, et al. Content-based image retrieval using color and texture fused features[J]. Mathematical and Computer Modeling, 2011, 54: 1121-1127.
doi: 10.1016/j.mcm.2010.11.044
[1] ZHANG Xiaoxia, LIU Fengkun, MAI Wei, MA Chongqi. Prediction of loom efficiency based on BP neural network and its improved algorithm [J]. Journal of Textile Research, 2020, 41(08): 121-127.
[2] WANG Hui, SUN Jie, DING Xiaojun, LONG Ying, ZOU Fengyuan. Comparison of feature extracting and matching methods for fabric patterns [J]. Journal of Textile Research, 2020, 41(04): 45-50.
[3] WAN Li, GONG Liying, JIA Minrui. Structural damage identification of intelligent composite materials based on principal component analysis [J]. Journal of Textile Research, 2019, 40(05): 53-58.
[4] . Selection of plaid fabric pattern based on interactive evaluation [J]. Journal of Textile Research, 2018, 39(09): 50-56.
[5] . Identification of fiber tensile fracture acoustic emission signal based on principal component analysis [J]. JOURNAL OF TEXTILE RESEARCH, 2018, 39(01): 19-24.
[6] . Classification of body shape based on longitudinal section curve [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(06): 86-91.
[7] . Structural damage evaluation of three-dimensional braided composite based on damage index [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(05): 69-74.
[8] . Clothing style recognition approach using Fourier descriptors and support vector machines [J]. JOURNAL OF TEXTILE RESEARCH, 2017, 38(05): 122-127.
[9] . Prediction of garment standard time based on processes similarity [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(11): 114-119.
[10] . Mechanism and software development for rainbow segment color yarn knitted fabrics [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(08): 47-53.
[11] . Continuity identification of fabric patterns [J]. JOURNAL OF TEXTILE RESEARCH, 2016, 37(08): 37-40.
[12] . Quantitative analysis of pore shape in spunbonded nonwovens [J]. JOURNAL OF TEXTILE RESEARCH, 2015, 36(08): 56-61.
[13] . Feature slice fitting and girth prediction using Elliptic Fourier [J]. JOURNAL OF TEXTILE RESEARCH, 2014, 35(8): 76-0.
[14] Xin YuWANG. Twist optimization of polypropylene filament /aramid wrapped yarn [J]. JOURNAL OF TEXTILE RESEARCH, 2014, 35(6): 40-0.
[15] PAN Ruru;GAO Weidong;LIU Jihong;WANG Hongbo. Automatic identification of woven fabric weave [J]. JOURNAL OF TEXTILE RESEARCH, 2010, 31(6): 43-47.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备14006648号
Copyright 2021 © Editorial office of Journal of Textile Research
Supported by Beijing Magtech Co.Ltd