Abstract
This paper proposes the recognition and classification of three dominant patterns of woven fabrics such as twill, satin and plain. The proposed classifier is based on the texture analysis of woven fabric images for the recognition. In the pattern recognition phase, three methods are tested and compared: Gabor wavelet, local binary pattern operators and gray-level co-occurrence matrices (GLCM). Taking advantage of the differences between the woven fabric textures, we adopt a technique which is based on the texture of the images in the pattern recognition phase. For the classification phase we used a support vector machine, which we have proven is a suitable classifier for this type of problem. The experimental results show that some of the studied methods are more compatible with this classification problem than others. Although it is the oldest method, GLCM always remains accurate (97.2%). The fusion of the Gabor wavelet and GLCM gives the best result (98%), but GLCM have the better running time.
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References
Ben Salem, Y., Nasri, S., Tourki, R.: Classification of tissues by neural network. In: 12th IEEE International Conference Electronics Circuits, and Systems, Proceedings of ICECS 2005, Gammarth, Tunisia
Boong S.J., Ji H.B.: Automatic recognition of woven fabric patterns by an artificial neural network. Textile Res. J. 73(7), 645–650 (2003)
Bugao X.: Identification fabric structures with fast Fourier transform techniques. Textile Res. J. 66(8), 496–506 (1996)
Burges C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 1–47 (1998)
Chung-Feng J.K., Cheng-chih T.: Automatic recognition of fabric nature by using the approach of texture analysis. Textile Res. J. 76(5), 375–382 (2006)
Davis L.S., Clearman M., Aggarwal J.K.: An empirical evaluation of generalized co-occurrence matrices. IEEE Trans. Pattern Anal. Mach. Intell. 3(2), 214–221 (1981)
Gaëlle Loosli: Toolbox SimpleSVM documentation. http://cbio.ensmp.fr/sirene/documentationSimpleSVM.pdf
Gotlieb C.C., Kreyszig H.E.: Texture descriptors based on co-occurrence matrices. Comput. Vis. Graph. Image Process 51(1), 70–86 (1990)
Haralick R.M., Shanmugam K., Dinstein I.: Textural features for image classification. IEEE Trans. Sys. Man. Cybern. 3(6), 610–621 (1973)
Huang C.C., Liu S.C., Yu W.H.: Woven fabric analysis by image processing. Part I: Identification of weave patterns. Textile Res. J. 70(6), 481–485 (2000)
Jensen K.L., Carstensen J.M.: Fuzz and pills evaluated on knitted textiles by image analysis. Textile Res. J. 72(1), 34–38 (2002)
Kang T.J., Kin S.M., Choi S.H.: Automatic recognition of fabric weave patterns by digital image analysis. Textile Res. J. 69(2), 77–83 (1999)
Manjunath B.S., Ma W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)
Ojala T., Pietikäinen M., Mäenpää T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–986 (2002)
Ravandi S.A.H., Toriumi K.: Fourier transform analysis of plain weave fabric appearance. Textile Res. J. 65(11), 676–683 (1995)
Vapnik V.: Statistical Learning Theory. Wiley, New York (1998)
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Ben Salem, Y., Nasri, S. Automatic recognition of woven fabrics based on texture and using SVM. SIViP 4, 429–434 (2010). https://doi.org/10.1007/s11760-009-0132-5
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DOI: https://doi.org/10.1007/s11760-009-0132-5