Abstract
Pum-Riang is a type of Thai silk with many patterns. Only experts can identify these patterns on sight. In order to help the general public who are interested in Pum-Riang silk, we propose an automatic Pum-Riang pattern detection using texture analysis. The process is divided into the feature extraction step, feature extraction step, and classifier training step. For each step, we compare various methods and parameters when applicable. The best model is evaluated on a separate test set. It achieves the perfect accuracy of 1.0, indicating that all test samples are correctly classified.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chakraborty, A., Chatterjee, S.M., Kumar, P.K.: Detection of lousiness in silk fabric using digital image processing. In: The 7th International Conference-TEXSCI 2010, pp. 6–8 (2010)
Frank, E., Hall, M., Witten, I.: The WEKA workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufman, Burlington (2016)
Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)
Jeon, B.S., Bae, J.H., Suh, M.W.: Automatic recognition of woven fabric patterns by an artificial neural network. Text. Res. J. 73(7), 645–650 (2003)
Kuo, S.C.Y., Lee, J.Y.: Automatic recognition of fabric weave patterns by a fuzzy C-means clustering method. Text. Res. J. 74(2), 107–111 (2004)
Mak, K.L., Peng, P., Yiu, K.: Fabric defect detection using morphological filters. Image Vis. Comput. 27(10), 1585–1592 (2009)
Mehta, R., Egiazarian, K.: Rotated local binary pattern (RLBP): rotation invariant texture descriptor. In: 2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013, Barcelona, Spain, 15.-18.2.2013, pp. 497–502. Institute of Electrical and Electronics Engineers IEEE (2013)
Ngan, H.Y., Pang, G.K., Yung, S., Ng, M.K.: Wavelet based methods on patterned fabric defect detection. Pattern Recogn. 38(4), 559–576 (2005)
Singh, S., Maurya, R., Mittal, A.: Application of complete local binary pattern method for facial expression recognition. In: 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI), pp. 1–4. IEEE (2012)
Tajeripour, F., Kabir, E., Sheikhi, A.: Fabric defect detection using modified local binary patterns. EURASIP J. Adv. Signal Process. 2008, 60 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Dittakan, K., Theera-Ampornpunt, N. (2018). Pum-Riang Thai Silk Pattern Classification Using Texture Analysis. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11013. Springer, Cham. https://doi.org/10.1007/978-3-319-97310-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-97310-4_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97309-8
Online ISBN: 978-3-319-97310-4
eBook Packages: Computer ScienceComputer Science (R0)