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

 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.
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.

## 小波变换下基于分形编码和 Zernike 矩的织物图像检索算法

• 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)
###### 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

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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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