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Fabric image retrieval algorithm based on fractal coding and Zernike moment under the wavelet transform

ZHANG Qin CAO Yi-qing

张琴, 曹一青. 小波变换下基于分形编码和 Zernike 矩的织物图像检索算法[J]. 中国光学(中英文), 2023, 16(3): 715-725. doi: 10.37188/CO.EN-2022-0021
引用本文: 张琴, 曹一青. 小波变换下基于分形编码和 Zernike 矩的织物图像检索算法[J]. 中国光学(中英文), 2023, 16(3): 715-725. doi: 10.37188/CO.EN-2022-0021
ZHANG Qin, CAO Yi-qing. Fabric image retrieval algorithm based on fractal coding and Zernike moment under the wavelet transform[J]. Chinese Optics, 2023, 16(3): 715-725. doi: 10.37188/CO.EN-2022-0021
Citation: ZHANG Qin, CAO Yi-qing. Fabric image retrieval algorithm based on fractal coding and Zernike moment under the wavelet transform[J]. Chinese Optics, 2023, 16(3): 715-725. doi: 10.37188/CO.EN-2022-0021

小波变换下基于分形编码和 Zernike 矩的织物图像检索算法

详细信息
  • 中图分类号: TN919.81

Fabric image retrieval algorithm based on fractal coding and Zernike moment under the wavelet transform

doi: 10.37188/CO.EN-2022-0021
Funds: Supported by National Youth Science Foundation of China (No. 62205168); Project of the Young and Middle-aged Teachers’ Education Research Projects of Fujian Province of China (No. JAT200534)
More Information
    Author Bio:

    ZHANG Qin (1988—), M.E, Lecturer, School of Mechatronics and Information Engineering, Putian University. Her research interests are in digital image retrieval and optical measurement. E-mail: daisyzhangq@126.com

    Corresponding author: daisyzhangq@126.com
  • 摘要:

    为帮助纺织企业的工作人员快速、准确地从数据库中检索出与织物图像相同或相似的图像,提出了一种小波变换下基于分形编码和 Zernike 矩的织物图像检索算法。首先,利用小波变换获得低频分量,对变换后的低频子图进行分形编码,得到编码参数。然后,计算低频子图像的 Zernike 矩。将小波变换下的分形编码参数和Zernike 矩相结合作为织物图像检索的特征量。相比于单特征检索方法,该算法克服了精度低、耗时长的问题。与基本分形算法(BFIC)、联合正交分形参数和改进的 Hu 不变矩算法(HVKF)以及稀疏分形图像压缩算法(SFIC)相比,该算法确保了重建图像的质量和较低的编码时间。实验结果表明,织物图像检索的平均精度和平均召回率均高于现有的检索方法。

     

  • Figure 1.  Part of the fabric images

    Figure 2.  Results of two-layer wavelet transform:(a) approximate coefficient ca2; (b) horizontal component chd2; (c) vertical component cvd; (d) diagonal component cdd2

    Figure 3.  Decoding images under different algorithms (from left to right are original image, BFIC, HVKF, SFIC and FZW results)

    Figure 4.  Comparison of decoding image quality under different algorithms

    Figure 5.  Precision-recall (P-R) curves under different algorithms

    Table  1.   Isometric transform

    j$q(j)$
    1Identity transformation
    2symmetry of the X axis
    3symmetry of the Y axis
    4Rotate 180 degrees
    5$y = - x$
    6$y = x$
    7Rotate 90 degrees counterclockwise
    8Rotate 270 degrees counterclockwise
    下载: 导出CSV

    Table  2.   Average PSNR of 3000 images with four different methods

    MethodBFICHVKFSFICFZW
    PSNR/dB28.2631.4736.3837.21
    下载: 导出CSV

    Table  3.   Comparison of decoding image quality and encoding time under different algorithms

    ImagesBFICHVKFSFICFZW
    PSNR/dBTime/sSSIMPSNR/dBTime/sSSIMPSNR/dBTime/sSSIMPSNR/dBTime/sSSIM
    Trellis28.44727.810.80532.72165.370.85237.8665.760.93835.8943.670.921
    Flower127.72748.320.74231.85148.880.82338.5383.630.95538.8243.850.978
    Cluster29.01733.700.84630.46160.530.86935.4890.080.93736.3038.230.945
    Stripes128.56742.240.78430.80156.600.85836.0682.340.94336.7138.130.960
    Leaves28.99736.590.80833.54163.470.88937.2571.090.94637.5742.970.966
    Stripes229.12740.680.81229.23163.260.85637.9387.720.93338.1544.280.974
    Flower228.70728.760.77430.53155.040.84238.2273.510.98637.6438.110.982
    Rhombus27.85730.050.69230.07163.770.87033.1082.550.96935.2142.930.983
    Flame28.13757.900.80231.07169.900.85836.3780.610.94437.2947.570.975
    Diamond27.32724.810.76930.32166.230.81034.7578.850.92638.4351.520.979
    Curve28.66675.550.80732.45147.960.87136.1863.480.93237.0635.920.968
    Dots29.57701.530.82132.76158.030.86436.9278.800.93938.2837.690.986
    Wave28.29681.630.79430.97150.540.80637.1576.050.94537.1838.500.964
    Scroll27.73717.840.65429.60161.860.79735.1079.370.89936.9938.030.943
    Twill129.54727.540.81131.55163.430.85337.2275.910.93837.6139.440.955
    Circle127.94720.090.76331.89159.870.84734.8873.000.91737.2640.160.967
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-10-31
  • 修回日期:  2022-11-29
  • 网络出版日期:  2023-04-18

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