Evaluation of texture feature based on basic local binary pattern for wood defect classification

(1) * Eihab Abdelkariem Bashir Ibrahim Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(2) Ummi Raba'ah Hashim Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(3) Lizawati Salahuddin Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(4) Nor Haslinda Ismail Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(5) Ngo Hea Choon Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(6) Kasturi Kanchymalay Mail (Centre for Advanced Computing Technology, Universiti Teknikal Malaysia Melaka, Malaysia)
(7) Siti Normi Zabri Mail (Centre for Telecommunication Research & Innovation, Universiti Teknikal Malaysia Melaka, Malaysia)
*corresponding author


Wood defects detection has been studied a lot recently to detect the defects on the wood surface and assist the manufacturers in having a clear wood to be used to produce a high-quality product. Therefore, the defects on the wood affect and reduce the quality of wood. This research proposes an effective feature extraction technique called the local binary pattern (LBP) with a common classifier called Support Vector Machine (SVM). Our goal is to classify the natural defects on the wood surface. First, preprocessing was applied to convert the RGB images into grayscale images. Then, the research applied the LBP feature extraction technique with eight neighbors (P=8) and several radius (R) values. After that, we apply the SVM classifier for the classification and measure the proposed technique's performance. The experimental result shows that the average accuracy achieved is 65% on the balanced dataset with P=8 and R=1. It indicates that the proposed technique works moderately well to classify wood defects. This study will consequently contribute to the overall wood defect detection framework, which generally benefits the automated inspection of the wood defects.




Article metrics

Abstract views : 849 | PDF views : 216




Full Text



[1] Hongbo Mu, Dawei Qi, Nan Hu, and Mingming Zhang, “Edge extraction of wood image with rot based on gray transformation,” in 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), 2010, pp. V2-605-V2-608, doi: 10.1109/ICCASM.2010.5620847.

[2] R. Qayyum, K. Kamal, T. Zafar, and S. Mathavan, “Wood Defects Classification Using GLCM Based Features And PSO Trained Neural Network,” 2016 22nd Int. Conf. Autom. Comput. (ICAC), pp. 3–7, 2016, doi: 10.1109/IConAC.2016.7604931.

[3] L. Wells, R. Gazo, R. Del Re, V. Krs, and B. Benes, “Defect detection performance of automated hardwood lumber grading system,” Comput. Electron. Agric., vol. 155, pp. 487–495, Sep. 2018, doi: 10.1016/j.compag.2018.09.025.

[4] U. R. Hashim et al., “Extraction and Exploratory Analysis of Texture Features on Images of Timber Defect,” Adv. Sci. Lett., vol. 4, no. 2, pp. 1104–1108, 2018, doi: 10.1166/asl.2018.10696.

[5] M. S. Packianather and B. Kapoor, “A wrapper-based feature selection approach using Bees Algorithm for a wood defect classification system,” 2015 10th Syst. Syst. Eng. Conf. SoSE 2015, pp. 498–503, 2015, doi: 10.1109/SYSOSE.2015.7151902.

[6] Z. Y. Xiang, Z. Y. Qin, L. Ying, J. L. Quan, and C. Z. Wei, “Identification of Wood Defects Based on LBP Features,” pp. 4202–4205, 2016, doi: 10.1109/ChiCC.2016.7554010

[7] A. Fahrurozi, S. Madenda, Ernastuti, and D. Kerami, “Wood Texture Features Extraction by Using GLCM Combined With Various Edge Detection Methods,” in International Congress on Theoretical and Applied Mathematics, Physics and Chemistry, 2016, doi: 10.1088/1742-6596/725/1/012005

[8] P. Barmpoutis, I. Barboutis, and P. Lefakis, “Detection of Various Characteristics on Wooden Surfaces , Using Scanner and Image Processing Techniques,” in 27th International Conference on Wood Modification and Tehnology, 2016, no. 2013, pp. 7–13. Available at: Google Scholar

[9] M. M. Hittawe, S. M. Muddamsetty, D. Sidibe, and F. Meriaudeau, “Multiple features extraction for timber defects detection and classification using SVM,” in 2015 IEEE International Conference on Image Processing (ICIP), 2015, vol. 2015-Decem, pp. 427–431, doi: 10.1109/ICIP.2015.7350834.

[10] A. Mahram, M. G. Shayesteh, and S. Jafarpour, “Classification of wood surface defects with hybrid usage of statistical and textural features,” in 2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012, pp. 749–752, doi: 10.1109/TSP.2012.6256397

[11] Z. Zhang, N. Ye, D. Wu, and Q. Ye, “Locating the Wood Defects with Typical Features and SVM,” in Proceedings of the 11th Join Conference on Information Sciences, 2008, pp. 1–7, doi:

[12] S. Li, W. Yuan, J. Yang, and D. Li, “Wood defect classification based on local binary difference excitation pattern,” Yi Qi Yi Biao Xue Bao/Chinese J. Sci. Instrum., vol. 40, no. 6, pp. 68–77, 2019, doi: 10.1109/ACCESS.2019.2945355.

[13] T. He, Y. Liu, Y. Yu, Q. Zhao, and Z. Hu, “Application of deep convolutional neural network on feature extraction and detection of wood defects,” Meas. J. Int. Meas. Confed., vol. 152, 2020, doi: 10.1016/j.measurement.2019.107357.

[14] X. Ji, H. Guo, and M. Hu, “Features extraction and classification of wood defect based on HU invariant moment and wavelet moment and BP neural network,” in ACM International Conference Proceeding Series, 2019, doi: 10.1145/3356422.3356459.

[15] K. Kamal, R. Qayyum, S. Mathavan, and T. Zafar, “Wood defects classification using laws texture energy measures and supervised learning approach,” Adv. Eng. Informatics, vol. 34, pp. 125–135, 2017, doi: 10.1016/j.aei.2017.09.007.

[16] W. Yuan, S. Li, and D. Li, “Wood surface crevice detection based on fusion of texture ridge line features,” Yi Qi Yi Biao Xue Bao/Chinese J. Sci. Instrum., vol. 38, no. 2, pp. 436–444, 2017, available at: Google Scholar.

[17] I. Of, W. Science, and I. N. Woodworking, “Detection of various characteristics on wooden surfaces , using scanner and,” no. 2013, pp. 7–13, 2016, available at: Google Scholar.

[18] Y. Zhang, S. Liu, J. Cao, C. Li, and H. Yu, “Wood board image processing based on dual-tree complex wavelet feature selection and compressed sensing,” Wood Sci. Technol., vol. 50, no. 2, pp. 297–311, 2016, doi: 10.1007/s00226-015-0776-y.

[19] Z. Peng, L. Yue, and N. Xiao, “Simultaneous Wood Defect and Species Detection with 3D Laser Scanning Scheme,” Int. J. Opt., vol. 2016, 2016, doi: 10.1155/2016/7049523.

[20] P. Barmpoutis, I. Barboutis, and P. Lefakis, “Detection of various characteristics on wooden surfaces, using scanner and image processing techniques,” in 27th International Conference on Wood Science and Technology, ICWST 2016: Implementation of Wood Science in Woodworking Sector - Proceedings, 2016, pp. 7–13, available at: Google Scholar.

[21] M. Mazen and S. M. Muddamsetty, “Multiple Features Extraction for Timber Defects Detection and Classification Using SVM,” Int. Conf. Image Process., pp. 427–431, 2015, doi: 10.1109/ICIP.2015.7350834.

[22] J. Zhang and C. Liu, “A study of a clothing image segmentation method in complex conditions using a features fusion model,” Automatika, vol. 61, no. 1, pp. 150–157, 2020, doi: 10.1080/00051144.2019.1691835.

[23] S. Gupta, J. Sarkar, M. Kundu, N. R. Bandyopadhyay, and S. Ganguly, “Automatic recognition of SEM microstructure and phases of steel using LBP and random decision forest operator,” Meas. J. Int. Meas. Confed., vol. 151, 2020, doi: 10.1016/j.measurement.2019.107224.

[24] K. Kaplan, Y. Kaya, M. Kuncan, M. R. Mi̇naz, and H. M. Ertunç, “An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis,” Appl. Soft Comput. J., vol. 87, 2020, doi: 10.1016/j.asoc.2019.106019.

[25] K. A. Khan, S. P. P., Y. U. Khan, and O. Farooq, “A hybrid Local Binary Pattern and wavelets based approach for EEG classification for diagnosing epilepsy,” Expert Syst. Appl., vol. 140, 2020, doi: 10.1016/j.eswa.2019.112895.

[26] Q. Zhang, H. Li, M. Li, and L. Ding, “Feature extraction of face image based on LBP and 2-D Gabor wavelet transform,” Math. Biosci. Eng., vol. 17, no. 2, pp. 1578–1592, 2020, doi: 10.3934/mbe.2020082.

[27] T. Ahonen, A. Hadid, and M. Pietikäinen, “Face Recognition with Local Binary Patterns,” in Computer Vision - ECCV 2004, 2004, pp. 469–481, doi: 10.1007/978-3-540-24670-1_36.

[28] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Gray scale and rotation invariant texture classification with local binary patterns,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2000, vol. 1842, pp. 404–420, doi: 10.1007/3-540-45054-8_27.

[29] U. R. Hashim, S. Z. Hashim, and A. K. Muda, “Image Collection for Non-Segmenting Approach of Timber Surface Defect Detection,” Int. J. Adv. Soft Comput. Its Appl., vol. 7, no. 1, pp. 15–34, 2015, available at: Google Scholar.

[30] Putra, A., Supriadi, S., Wibawa, A., Pranolo, A., and A. Gaffar, "Modification of a gray-level dynamic range based on a number of binary bit representation for image compression," Science in Information Technology Letters, vol. 1, no. 1, pp. 9-16, 2020, doi: 10.31763/sitech.v1i1.17.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Advances in Intelligent Informatics
ISSN 2442-6571  (print) | 2548-3161 (online)
Organized by UAD and ASCEE Computer Society
Published by Universitas Ahmad Dahlan
W: http://ijain.org
E: info@ijain.org (paper handling issues)
   andri.pranolo.id@ieee.org (publication issues)

View IJAIN Stats

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0