Abstract
Fabric texture classification can be implemented automatically using fabric texture analysis using their images. Texture analysis using fabric images has lots of applications such as automatic recognition and classification of fabrics and to detect the defects on the fabrics and the quality of the fabrics in fabric industries. Conversely, for the existing manual systems, it is a tough task to estimate the correct fabric texture class group effectively. Manual inspection procedures are inefficient for classification due to lack of vigilance and boredom which deteriorates performance. To reduce the cost and time, an automated classification is required based on computer vision and image processing techniques. In this study, a well-organized fabric texture classification system is proposed. Based on the feature set values, the present paper proposes a user-defined approach to classify the fabric texture image into one of the familiar five pre-defined classes (silk, cotton, linen, wool and worsted).
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References
Chi Hau, C., Louis-francois, P., Patrick, S.P.W.: Handbook of Pattern Recognition and Computer Vision, 2nd edn. World Scientific, Singapore (2000)
Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man, Cybern. 3(6), 610–621 (1973)
Cohen, F.S., Fan, Z., Attali, S.: Automated inspection of textile fabrics using textural models. IEEE Trans. Pattern Analy. Mach. Intell. 13(8), 803–808 (1991)
Sutton, R.N., Hall, E.L.: Texture measures for automatic classification of pulmonary disease. IEEE Trans. Comput. C-21(7), 667–676 (1972)
Harry, H., Gunzer, U., Hans, M.: Combined local color and texture analysis of stained cells. Comput. Vis. Graph. Image Process. 33(3), 364–376 (1986)
Raghu, P.P., Yegnanarayana, B.: Segmentation of gabor filtered textures using deterministic relaxation. IEEE Trans. Image Process. 5(12), 1625–1636 (1996)
Vijaya Kumar, V., Eswara, B., Raju U.S.N. Suresh, A.: Classification of textures by avoiding complex patterns. J. Comput. Sci. 4(2), 133–138 (2008)
Sonawane, C., Singh, D.P., Sharma, R., Nigam, A., Bhavsar, A.: Fabric classification and matching using CNN and Siamese network for E-commerce. In: Vento, M., Percannella, G. (eds.) CAIP 2019, LNCS, vol. 11679, pp. 193–205. Springer, Cham (2019)
Kumar, P., Sekhararao, V.C. , Ramadevi, A., Swathi, C.N.D., Mallidi, P.R.R.: Texture primitive unit extraction using different wavelet transforms for texture classification. In: 2nd International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), pp. 177–181. IEEE, Bangalore, India (2016)
Chang, T., Kuo, C.C.J.: Texture analysis and classification with tree-structured wavelet transform. IEEE Trans. Image Process. 2(4), 429–442 (1993)
Kumar, P., Devi, D.R., Kumar, D.J.N., Murty, A.V.S.N.: Alphabet pattern approach for color fabric texture classification. In: International Conference on Computer Communication and Informatics (ICCCI 2017), pp. 1–6. IEEE, Coimbatore (2017)
Faugeras, O.D., Pratt, W.K.: Decorrelation Methods of Texture Feature Extraction. IEEE Trans. Pattern Analy. Mach. Intell. 2(4), 323–332 (1980)
Derin, H., Elliot, H.: Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans. Pattern Analy. Mach. Intell. 9(1), 39–59 (1987)
Wang, Z.Z., Yong, J.H.: Texture analysis and classification with linear regression model based on wavelet transform. IEEE Trans. Image Process. 17(8), 1421–1430 (2008)
Murthy, G.S.N., Srinivasa Rao, V.: A novel approach based on decreased dimension and reduced gray level range matrix features for stone texture classification. Int. J. Electr. Comput. Eng. 7(5), 2502–2513 (2017)
Murthy, G.S.N., Srinivasa Rao, V., Vanitha, K.: An effective stone image classification using surface patterns based on reduced dimension and Gray level range model. Int. J. Adv. Res. Comput. Sci. 8(5), 1758–1765 (2017)
Murthy, G.S.N., Srinivasa Rao, V., Kumar, P.: A new approach for stone texture classification using shape features on Texton. Adv. Comput. Sci. Technol. 10(3), 363–378 (2017)
Murthy, G.S.N., Rao, V., Kumar, P., Ganesh, S.S.: Texture unit pattern approach for stone texture classification. Int. J. Appl. Eng. Res. 12(24), 14434–14440 (2017)
Vijaya Kumar, V., Murty, G.S.N., Kumar, P.: Classification of facial expressions based on transitions derived from third order neighborhood LBP. Global J. Comput. Sci. Technol. (F) 14(1), 1–12 (2014)
Partner Textile Homepage, http://www.partnertextile.com/. Last Accessed on 9 Oct 2016
Tabassian, M., Ghaderi, R., Ebrahimpour, R.: Knitted fabric defect classification for uncertain labels based on Dempster-Shafer theory of evidence. Expert Syst. Appl. 38(5), 5259–5267 (2011)
Suhair, H.S., Kilidhar, A.L., Loay, E.G.: Texture recognition using co-occurrence matrix features and neural network. J. Theor. Appl. Inf. Technol. 95(21), 5949–5961 (2017)
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Murthy, G.S.N., Kumar, P.S., Satya Kumari, T., Veerraju, T., Nagendra Kumar, D.J., Murthy, C.S. (2022). Texture Unit Pattern Approach for Fabric Classification. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_5
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DOI: https://doi.org/10.1007/978-981-19-0840-8_5
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