| 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
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A fabric image retrieval algorithm based on fractal coding and Zernike moments under a wavelet transform is proposed, which can quickly and accurately retrieve images from a database that are similar to fabric images submitted for retrieval. Firstly, the low-frequency component is obtained by a wavelet transform, and the transformed low-frequency sub-image is fractally encoded to obtain its coding parameters. Then, the Zernike moment of the low-frequency sub-image is calculated. The fractal coding parameters and Zernike moment under a wavelet transform are combined as the fabric image retrieval characteristic. The algorithm overcomes the problems of low retrieval accuracy and the high time consumption of direct feature extraction under a single feature. Compared with the Basic Fractal Image Compression (BFIC) algorithm, the joint orthogonal fractal parameters with the improved Hu invariant moment and Variable bandwidth Kernel density estimation of Fractal parameters (HVKF) algorithm and the Sparse Fractal Image Compression (SFIC) algorithm, the proposed algorithm ensures the quality and lower encoding time of the reconstructed image. The experiments show that the average precision and average recall of fabric image retrieval are higher than those of existing methods.
| [1] |
SINGH C, KAUR K P. A fast and efficient image retrieval system based on color and texture features[J].
Journal of Visual Communication and Image Representation, 2016, 41: 225-238.
doi:10.1016/j.jvcir.2016.10.002
|
| [2] |
LEE J, SUL I. Construction of garment pattern shape information system using image analysis and shape recognition techniques[J].
International Journal of Clothing Science and Technology, 2016, 28(4): 543-555.
doi:10.1108/IJCST-10-2015-0114
|
| [3] |
HU X D, FU M Y, ZHU ZH J,
et al. Unsupervised defect detection algorithm for printed fabrics using content-based image retrieval techniques[J].
Textile Research Journal, 2021, 91(21-22): 2551-2566.
doi:10.1177/00405175211008614
|
| [4] |
ZHANG N, XIANG J, WANG L,
et al. Image retrieval of wool fabric. Part II: based on low-level color features[J].
Textile Research Journal, 2020, 90(7-8): 797-808.
doi:10.1177/0040517519881819
|
| [5] |
XIANG J, ZHANG N, PAN R R,
et al. Fabric retrieval based on multi-task learning[J].
IEEE Transactions on Image Processing, 2021, 30: 1570-1582.
doi:10.1109/TIP.2020.3043877
|
| [6] |
JIANG D Y, KIM J. Image retrieval method based on image feature fusion and discrete cosine transform[J].
Applied Sciences, 2021, 11(12): 5701.
doi:10.3390/app11125701
|
| [7] |
KHALID M J, IRFAN M, ALI T,
et al. Integration of discrete wavelet transform, DBSCAN, and classifiers for efficient content based image retrieval[J].
Electronics, 2020, 9(11): 1886.
doi:10.3390/electronics9111886
|
| [8] |
PAN R R, GAO W D, LI W,
et al. Image analysis for seam-puckering evaluation[J].
Textile Research Journal, 2017, 87(20): 2513-2523.
doi:10.1177/0040517516673330
|
| [9] |
LIU P ZH, GUO J M, CHAMNONGTHAI K,
et al. Fusion of color histogram and LBP-based features for texture image retrieval and classification[J].
Information Sciences, 2017, 390: 95-111.
doi:10.1016/j.ins.2017.01.025
|
| [10] |
XIN S, SONG ZH G, SHI J L,
et al. Multiple channels local binary pattern for color texture Representation and classification[J].
Signal Processing:
Image Communication, 2021, 98: 116392.
doi:10.1016/j.image.2021.116392
|
| [11] |
JAMIL N, SOH H C, SEMBOK T M T,
et al.. A modified edge-based region growing segmentation of geometric objects[C]//
Lecture Notes in Computer Science. Berlin: Springer-Verlag, 2011: 99.
|
| [12] |
FU B L, LIU X G. An intelligent computational framework for the definition and identification of the womenswear silhouettes[J].
International Journal of Clothing Science and Technology, 2019, 31(2): 158-180.
doi:10.1108/IJCST-08-2017-0128
|
| [13] |
CORPUS G, PIÑERO D P. Short-term effect of wearing of extended depth-of-focus contact lenses in myopic children: a pilot study[J].
Applied Sciences, 2022, 12(1): 431.
doi:10.3390/app12010431
|
| [14] |
BAR O, BIBRZYCKI Ł, NIEDŹWIECKI M,
et al. Zernike moment based classification of cosmic ray candidate hits from CMOS sensors[J].
Sensors, 2021, 21(22): 7718.
doi:10.3390/s21227718
|
| [15] |
YU X L, WANG H L. Support vector machine classification model for color fastness to ironing of vat dyes[J].
Textile Research Journal, 2021, 91(15-16): 1889-1899.
doi:10.1177/0040517521992366
|
| [16] |
FAYAZ M, TOROKELDIEV N, TURDUMAMATOV S,
et al. An efficient methodology for brain MRI classification based on DWT and convolutional neural network[J].
Sensors, 2021, 21(22): 7480.
doi:10.3390/s21227480
|
| [17] |
DARAEE F, MOZAFFARI S. Watermarking in binary document images using fractal codes[J].
Pattern Recognition Letters, 2014, 35: 120-129.
doi:10.1016/j.patrec.2013.04.022
|
| [18] |
AHMAD M, AGARWAL S, ALKHAYYAT A,
et al. An image encryption algorithm based on new generalized fusion fractal structure[J].
Information Sciences, 2022, 592: 1-20.
doi:10.1016/j.ins.2022.01.042
|
| [19] |
JAGANNADHAM D B V, RAJU G V S, NARAYANA D V S. Novel performance analysis of DCT, DWT and fractal coding in image compression[M]//RAJU K S, SENKERIK R, LANKA S P,
et al.. Data Engineering and Communication Technology. Singapore: Springer, 2020: 611-622.
|
| [20] |
HUANG X Q, ZHANG Q, LIU W B. A new method for image retrieval based on analyzing fractal coding characters[J].
Journal of Visual Communication and Image Representation, 2013, 24(1): 42-47.
doi:10.1016/j.jvcir.2012.10.005
|
| [21] |
ZHANG Q, HUANG X Q, LIU W B,
et al. An effective image retrieval method based on Kernel Density Estimation of collage error and moment invariants[J].
Journal of Electronics(
China), 2013, 30(4): 391-400.
doi:10.1007/s11767-013-3031-4
|
| [22] |
TEAGUE M R. Image analysis via the general theory of moments[J].
Journal of the Optical Society of America, 1980, 70(8): 920-930.
|
| [23] |
WANG Y, ZHAO Y SH, CHEN Y. Texture classification using rotation invariant models on integrated local binary pattern and Zernike moments[J].
Eurasip Journal on Advances in Signal Processing, 2014, 2014(1): 182.
doi:10.1186/1687-6180-2014-182
|
| [24] |
SWAIN M, SWAIN D. An effective watermarking technique using BTC and SVD for image authentication and quality recovery[J].
Integration, 2022, 83: 12-23.
|
| [25] |
WANG ZH, BOVIK A C, SHEIKH H R,
et al. Image quality assessment: from error visibility to structural similarity[J].
IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
doi:10.1109/TIP.2003.819861
|
| [26] |
WU ZH H, SONG T T, ZHANG Y B. Quantum
k-means algorithm based on Manhattan distance[J].
Quantum Information Processing, 2022, 21(1): 19.
doi:10.1007/s11128-021-03384-7
|
| [27] |
JIANG X P, HU X H, HE T T. Identification of the clustering structure in microbiome data by density clustering on the Manhattan distance[J].
Science China Information Sciences, 2016, 59(7): 070104.
doi:10.1007/s11432-016-5587-8
|
| [28] |
FU G H, XU F, ZHANG B Y,
et al. Stable variable selection of class-imbalanced data with precision-recall criterion[J].
Chemometrics and Intelligent Laboratory Systems, 2017, 171: 241-250.
doi:10.1016/j.chemolab.2017.10.015
|
| [29] |
ZHANG Q, LIN Q H, KANG X. Research on image retrieval based on kernel density estimation and fractal coding algorithm[J].
Acta Metrologica Sinica, 2017, 38(3): 284-287. (in Chinese)
doi:10.3969/j.issn.1000-1158.2017.03.07
|
| [30] |
WANG J J, CHEN P, XI B,
et al. Fast sparse fractal image compression[J].
PLoS One, 2017, 12(9): e0184408.
doi:10.1371/journal.pone.0184408
|
| [31] |
ZHA T. Application comparison of textile fabric image retrieval algorithms based on content[J].
Journal of Textile Science&
Fashion Technology, 2020, 7(2): 659.
|
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