Image Classification with Shell Texture Feature Extraction Using Local Binary Pattern (LBP) Method
Keywords:texture feature extraction, LBP, classification, shell image
Classification procedure that is usually done manually by way of separation based on the texture of the shell shell. Classification is done by looking at objects based on inherent characteristics usually referred to as features / characteristics. Classification by hand can cause accuracy problems. In the image of the shells, texture characteristics are needed to distinguish one type of shell from another. The purpose of this study is to develop a texture feature extraction system for the classification of shell images. The input image is carried out preprocessing and segmenting to separate objects from the background and the image of the separated object is transformed into a grayscale image for the feature extraction process using the Local Binary Pattern method. Based on trials that have been done, the accuracy is quite good, the highest accuracy value occurs in shellfish blood cockles with RBF kernels. While the lowest accuracy is on testing the feather shell image where the accuracy value is 86.6% this result can show that the LBP method with SVM classification is quite reliable in calculating the accuracy for the classification process of shellfish types.
R. Hudaya, “Pengaruh Pemberian Belimbing wuluh (Averrhoa bilimbi) Terhadap Kadar Kadmium (Cd) Pada Kerang (Bivalvia) Yang Berasal Dari Laut Belawan Tahun 2010,” 2010.
N. Afiati, “Karakteristik Pertumbuhan Alometri Cangkang Kerang Darah Anadara Indica (L.)(Bivalvia: Arcidae),” J. Saintek Perikan., vol. 1, no. 2, pp. 45–52, 2005.
A. S. Nugroho, A. B. Witarto, and D. Handoko, “Application of support vector machine in bioinformatics,” in Proceeding of Indonesian Scientific Meeting in Central Japan, 2003, p. 2003.
R. P. N. Budiarti, S. Sukaridhoto, M. Hariadi, and M. H. Purnomo, “Big Data Technologies using SVM (Case Study: Surface Water Classification on Regional Water Utility Company in Surabaya),” in 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE), 2019, pp. 94–101.
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971–987, 2002.
S. Ke-Chen, Y. A. N. Yun-Hui, C. Wen-Hui, and X. Zhang, “Research and perspective on local binary pattern,” Acta Autom. Sin., vol. 39, no. 6, pp. 730–744, 2013.
M. Pietikäinen, A. Hadid, G. Zhao, and T. Ahonen, Computer vision using local binary patterns, vol. 40. Springer Science & Business Media, 2011.
Y. Cao, S. Pranata, and H. Nishimura, “Local binary pattern features for pedestrian detection at night/dark environment,” in 2011 18th IEEE International Conference on Image Processing, 2011, pp. 2053–2056.
L. Nanni, A. Lumini, and S. Brahnam, “Local binary patterns variants as texture descriptors for medical image analysis,” Artif. Intell. Med., vol. 49, no. 2, pp. 117–125, 2010.
S. Jabri, M. Saidallah, A. E. B. El Alaoui, and A. El Fergougui, “Moving Vehicle Detection Using Haar-like, LBP and a Machine Learning Adaboost Algorithm,” in 2018 IEEE International Conference on Image Processing, Applications and Systems (IPAS), 2018, pp. 121–124.
B.-G. Han, J. T. Lee, K.-T. Lim, and Y. Chung, “Real-Time License Plate Detection in High-Resolution Videos Using Fastest Available Cascade Classifier and Core Patterns,” Etri J., vol. 37, no. 2, pp. 251–261, 2015.
Y. M. G. Costa, L. S. Oliveira, A. L. Koerich, F. Gouyon, and J. G. Martins, “Music genre classification using LBP textural features,” Signal Processing, vol. 92, no. 11, pp. 2723–2737, 2012.
D. Huang, C. Shan, M. Ardabilian, Y. Wang, and L. Chen, “Local binary patterns and its application to facial image analysis: a survey,” IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev., vol. 41, no. 6, pp. 765–781, 2011.
J. Sima, Y. Dong, T. Wang, L. Zheng, and J. Pu, “Extended contrast local binary pattern for texture classification,” Int. J. New Technol. Res., vol. 4, no. 3, 2018.
H. Maruta, A. Nakamura, and F. Kurokawa, “A new approach for smoke detection with texture analysis and support vector machine,” in 2010 IEEE International Symposium on Industrial Electronics, 2010, pp. 1550–1555.
How to Cite
Copyright (c) 2020 Putri Aisyiyah Rakhma Devi, Rizqi Putri Nourma Budiarti
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.