Texture Representation and Application of Colored Spun Fabric Using Uniform Three-Structure Descriptor

• Autorzy: Li Yuan , Muli Liu , Liu JunPing , Yang Yali , Gong Xue

Identyfikatory

DOI

10.2478/aut-2021-0039

• Języki publikacji: EN

Abstrakty

EN

The local binary pattern (LBP) and its variants have shown their effectiveness in texture images representation. However, most of these LBP methods only focus on the histogram of LBP patterns, ignoring the spatial contextual information among them. In this paper, a uniform three-structure descriptor method was proposed by using three different encoding methods so as to obtain the local spatial contextual information for characterizing the nonuniform texture on the surface of colored spun fabrics. The testing results of 180 samples with 18 different color schemes indicate that the established texture representation model can accurately express the nonuniform texture structure of colored spun fabrics. In addition, the overall correlation index between texture features and sample parameters is 0.027 and 0.024, respectively. When compared with the LBP and its variants, the proposed method obtains a higher representational ability, and simultaneously owns a shorter time complexity. At the same time, the algorithm proposed in this paper enjoys ideal effectiveness and universality for fabric image retrieval. The mean Average Precision (mAP) of the first group of samples is 86.2%; in the second group of samples, the mAP of the sample with low twist coefficient is 89.6%, while the mAP of the sample with high twist coefficient is 88.5%.

Słowa kluczowe

EN

colored spun fabric; non-uniform texture; texture representation; three-structure descriptor; uniform mode

PL

tkanina; niejednorodna tekstura; deskryptor trzech struktur

• Wydawca: Lodz University of Technology, Faculty of Material Technologies and Textile Design
• Czasopismo: Autex Research Journal
• Rocznik: 2022
• Tom: Vol. 22, no. 4
• Strony: 477--487
• Opis fizyczny: Bibliogr. 13 poz.

Twórcy

autor

Li Yuan

• School of electronic and electrical engineering, Wuhan Textile University, Wuhan 430200, China
• Wuhan Textile University, Hubei Province, new textile materials and technology State Key Laboratory of advanced processing technology, Wuhan 430200, Hubei Province

autor

Muli Liu

• School of electronic and electrical engineering, Wuhan Textile University, Wuhan 430200, China

autor

Liu JunPing

• Wuhan Textile University School of mathematics and computer, Wuhan 430200, China

autor

Yang Yali

• School of electronic and electrical engineering, Wuhan Textile University, Wuhan 430200, China

autor

Gong Xue

• School of electronic and electrical engineering, Wuhan Textile University, Wuhan 430200, China

Bibliografia

• [1] Li, L., Ling-Jun, Z., Cheng-Yu, G., Liang, W., Jun, T. (2018). Texture classification: State-of-the-art methods and prospects. Acta Automatica Sinica, 44(4), 584-607.
• [2] Xiao, B., Wang, K., Bi, X., Li, W., Han, J. (2019). 2D-LBP: An enhanced local binary feature for texture image classification. IEEE Transactions on Circuits and Systems for Video Technology, 29(9), 2796-2808.
• [3] Pan, Z., Wu, X., Li, Z. (2019). Central pixel selection strategy based on local gray-value distribution by using gradient information to enhance LBP for texture classification. Expert Systems with Applications, 120, 319-334.
• [4] Zhao, Y., Wang, R. G., Wang, W. M., Gao, W. (2016). Local Quantization Code histogram for texture classification. Neurocomputing, 207, 354-364.
• [5] Zhou, J., Wang, J. G., Gao, W. D. (2018). Unsupervised fabric defect segmentation using local texture feature. Journal of Textile Research, 39, 57-64.
• [6] Merabet, E. M., Ruichek, Y. (2018). Local concave-and-convex micro-structure patterns for texture classification. Pattern Recognition, 76(C), 303-322.
• [7] Banerjee, P., Bhunia, A. K., Bhattacharyya, A., Roy, P. P., Murala, S. (2018). Local meighborhood intensity pattern–A new texture feature descriptor for image retrieval. Expert Systems with Applications, 113, 100-115.
• [8] Veerashetty, S., Patil, N. B. (2020). Novel LBP based texture descriptor for rotation, illumination and scale invariance for image texture analysis and classification using multi-kernel SVM. Multimedia Tools and Applications, 79(15), 9935-9955.
• [9] Zhang, Z., Liu, S., Mei, X., Xiao, B., Zheng, L. Learning completed discriminative local features for texture classification. Pattern Recognition, 67, 263-275.
• [10] Lan, R., Zhou, Y., Tang, Y. Y. (2016). Quaternionic local ranking binary pattern: A local descriptor of color images. IEEE Transactions on Image Processing, 25(2), 566-579.
• [11] Wang, G. D., Zhang, P. L., Ren, G. Q., Kou, X. (2012). Texture feature extraction method fused with LBP and GLCM. Computer Engineering, 38(5), 199-201.
• [12] Wang, K. L., Zhang, Y. H., Xiao, B., Li, W. S. Texture images classification based on two dimensional local binary patterns. Acta Electronica Sinica, 46(10), 2519-2526.
• [13] Shah, D., Zaveri, T. (2021). Hyperspectral endmember extraction using Pearson’s correlation coefficient. International Journal of Computational Science and Engineering, 24(1), 89-97.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).