Features Optimization of Gray Level Co-Occurrence Matrix by Artificial Bee Colony Algorithm for Texture Classification
Keywords:
GLCM parameters optimization, Artificial Bee Colony Algorithm, Texture Classification, Multi-layer Perceptron Neural NetworkAbstract
Gray Level Co-occurrence Matrix (GLCM) is one of the most popular texture analysis methods. The fundamental issue of GLCM is the suitable selection of input parameters, where many researchers depended on trial and observation approach for selecting the best combination of GLCM parameters to improve the texture classification, which is tedious and time-consuming. This paper proposes a new optimization method for the GLCM parameters using Artificial Bee Colony Algorithm (ABC) to improve the binary texture classification. For the testing, 13 Haralick features were extracted from the UMD database, which has been used with the multi-layer perceptron neural network classifier. The experimental results proved that, the proposed method has been succeeded to finding the best combination of GLCM parameters that leads to the best binary texture classification accuracy performance.
Downloads
References
M. Ayan, O. A. Erdem, and H. Ş. Bilge, "Multi-featured content-based image retrieval using color and texture features," in Applied Mechanics and Materials, 2016, pp. 136-143. DOI: https://doi.org/10.4028/www.scientific.net/AMM.850.136
A. K. Jain and A. Vailaya, "Image retrieval using color and shape," Pattern recognition, vol. 29, pp. 1233-1244, 1996. DOI: https://doi.org/10.1016/0031-3203(95)00160-3
S. E. Umbaugh, Computer imaging: digital image analysis and processing: CRC press, 2005.
A. Z. Y. Yeo, "Texture classification using wavelet transform and support vector machines," UTAR, 2011.
L. Lepistö, I. Kunttu, J. Autio, and A. Visa, "Rock image classification using non-homogenous textures and spectral imaging," 2003.
J. Y. Tou, Y. H. Tay, and P. Y. Lau, "A comparative study for texture classification techniques on wood species recognition problem," in Natural Computation, 2009. ICNC'09. Fifth International Conference on, 2009, pp. 8-12. DOI: https://doi.org/10.1109/ICNC.2009.594
Y. B. Salem and S. Nasri, "Texture classification of woven fabric based on a GLCM method and using multiclass support vector machine," in Systems, Signals and Devices, 2009. SSD'09. 6th International Multi-Conference on, 2009, pp. 1-8. DOI: https://doi.org/10.1109/SSD.2009.4956737
S. Hu, C. Xu, W. Guan, Y. Tang, and Y. Liu, "Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis," Bio-medical materials and engineering, vol. 24, pp. 129-143, 2014. DOI: https://doi.org/10.3233/BME-130793
H. Murray, A. Lucieer, and R. Williams, "Texture-based classification of sub-Antarctic vegetation communities on Heard Island," International Journal of Applied Earth Observation and Geoinformation, vol. 12, pp. 138-149, 2010. DOI: https://doi.org/10.1016/j.jag.2010.01.006
M. Tuceryan and A. K. Jain, "Texture analysis," Handbook of pattern recognition and computer vision, vol. 2, pp. 207-248, 1993. DOI: https://doi.org/10.1142/9789812384737_0007
T. J. Yi, "Texture classification on wood images for species recognition," Citeseer, 2009.
R. W. Conners and C. A. Harlow, "A theoretical comparison of texture algorithms," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 204-222, 1980. DOI: https://doi.org/10.1109/TPAMI.1980.4767008
T. Ojala, M. Pietikäinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern recognition, vol. 29, pp. 51-59, 1996. DOI: https://doi.org/10.1016/0031-3203(95)00067-4
R. M. Haralick and K. Shanmugam, "Textural features for image classification," IEEE Transactions on systems, man, and cybernetics, pp. 610-621, 1973.
M. Venkataramana, S. E. Reddy, C. Satyanarayana, and S. Anuradha, "A Review of Recent Texture Classification: Methods," IOSR Journal of Computer Engineering (IOSRJCE7), vol. 14, p. 1, 2013. DOI: https://doi.org/10.9790/0661-1415460
Y. Kaya, L. Kayci, R. Tekin, and Ö. Faruk Ertuğrul, "Evaluation of texture features for automatic detecting butterfly species using extreme learning machine," Journal of Experimental & Theoretical Artificial Intelligence, vol. 26, pp. 267-281, 2014. DOI: https://doi.org/10.1080/0952813X.2013.861875
Zhang, J. Cui, W. Wang, and C. Lin, "A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm," Sensors, vol. 17, p. 1474, 2017. DOI: https://doi.org/10.3390/s17071474
Ş. Öztürk and B. Akdemir, "Application of Feature Extraction and Classification Methods for Histopathological Image using GLCM, LBP, LBGLCM, GLRLM and SFTA," Procedia Computer Science, vol. 132, pp. 40-46, 2018. DOI: https://doi.org/10.1016/j.procs.2018.05.057
F. R. De Siqueira, W. R. Schwartz, and H. Pedrini, "Multi-scale gray level co-occurrence matrices for texture description," Neurocomputing, vol. 120, pp. 336-345, 2013. DOI: https://doi.org/10.1016/j.neucom.2012.09.042
J. Y. Tou, Y. H. Tay, and P. Y. Lau, "One-dimensional grey-level co-occurrence matrices for texture classification," in Information Technology, 2008. ITSim 2008. International Symposium on, 2008, pp. 1-6. DOI: https://doi.org/10.1109/ITSIM.2008.4631992
B. Pathak and D. Barooah, "Texture analysis based on the gray-level co-occurrence matrix considering possible orientations," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 2, pp. 4206-4212, 2013.
D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical report-tr06, Erciyes university, engineering faculty, computer …2005.
M. B. Othmen, M. Sayadi, and F. Fnaiech, "Interest of the multi-resolution analysis based on the co-occurrence matrix for texture classification," in MELECON 2008-The 14th IEEE Mediterranean Electrotechnical Conference, 2008, pp. 852-856. DOI: https://doi.org/10.1109/MELCON.2008.4618542
J. Y. Tou, K. K. Y. Khoo, Y. H. Tay, and P. Y. Lau, "Evaluation of Speed and Accuracy for Comparison of Texture Classification Implementation on Embedded Platform," ed: Citeseer, 2009.
P. Brodatz, Textures: a photographic album for artists and designers: Dover Pubns, 1966.
R. A. Pramunendar, C. Supriyanto, D. H. Novianto, I. N. Yuwono, G. F. Shidik, and P. N. Andono, "A classification method of coconut wood quality based on Gray Level Co-occurrence matrices," in Robotics, Biomimetics, and Intelligent Computational Systems (ROBIONETICS), 2013 IEEE International Conference on, 2013, pp. 254-257. DOI: https://doi.org/10.1109/ROBIONETICS.2013.6743614
S. Dash, K. Chiranjeevi, U. Jena, and A. Trinadh, "Comparative study of image texture classification techniques," in 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 2015. DOI: https://doi.org/10.1109/EESCO.2015.7253732
L.-K. Soh and C. Tsatsoulis, "Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices," IEEE Transactions on geoscience and remote sensing, vol. 37, pp. 780-795, 1999. DOI: https://doi.org/10.1109/36.752194
A. F. Hasan, M. F. Ahmad, M. N. Ayob, S. A. A. Rais, N. H. Saad, A. Faiz, et al., "Application of binary particle swarm optimization in automatic classification of wood species using gray level co-occurrence matrix and k-nearest neighbor," Int. J. Sci. Eng. Res, vol. 4, pp. 50-55, 2013.
Ü. H. Atasever, C. Özkan, and F. Sunar, "The use of artificial intelligence optimization algorithms in unsupervised classification," ed: EARSeL, 2011.
D. J. Sathya and K. Geetha, "Mass classification in breast DCE-MR images using an artificial neural network trained via a bee colony optimization algorithm," ScienceAsia, vol. 39, pp. 294-305, 2013. DOI: https://doi.org/10.2306/scienceasia1513-1874.2013.39.294
S. Shanthi and V. M. Bhaskaran, "Modified artificial bee colony based feature selection: a new method in the application of mammogram image classification," Int. J. Sci. Eng. Technol. Res, vol. 3, pp. 1664-1667, 2014.
M. S. Uzer, N. Yilmaz, and O. Inan, "Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification," The Scientific World Journal, vol. 2013, 2013. DOI: https://doi.org/10.1155/2013/419187
P. Arora, H. Kundra, and V. Panchal, "Fusion of biogeography based optimization and artificial bee colony for identification of natural terrain features," Editorial Preface, vol. 3, 2012. DOI: https://doi.org/10.14569/IJACSA.2012.031018
M. S. Hitam, F. M. Albkosh, and W. N. J. H. W. Yussof, "Optimization of Image Features using Artificial Bee Colony Algorithm and Multi-layered Perceptron Neural Network for Texture Classification," Journal of Telecommunication, Electronic and Computer Engineering (JTEC), vol. 9, pp. 201-206, 2017.
F. M. Albkosh, M. S. Hitam, W. N. J. H. W. Yussof, A. A. K. A. Hamid, and R. Ali, "Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification," International Journal of Electrical and Computer Engineering, vol. 9, p. 5253, 2019. DOI: https://doi.org/10.11591/ijece.v9i6.pp5253-5262
R. M. Haralick, K. Shanmugam, and I. H. Dinstein, "Textural features for image classification," Systems, Man and Cybernetics, IEEE Transactions on, pp. 610-621, 1973. DOI: https://doi.org/10.1109/TSMC.1973.4309314
D. Karaboga and B. Akay, "A comparative study of artificial bee colony algorithm," Applied mathematics and computation, vol. 214, pp. 108-132, 2009. DOI: https://doi.org/10.1016/j.amc.2009.03.090
S. Hossain and S. Serikawa, "Texture databases–a comprehensive survey," pattern recognition letters, vol. 34, pp. 2007-2022, 2013. DOI: https://doi.org/10.1016/j.patrec.2013.02.009
G. S. Raghtate and S. S. Salankar, "Comparison of Second Order Statistical Analysis and Wavelet Transform Method for Texture Image Classification," in Computational Intelligence and Communication Networks (CICN), 2015 International Conference on, 2015, pp. 318-323. DOI: https://doi.org/10.1109/CICN.2015.70
Downloads
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2021 فتحي مفتاح عبدالرزاق البكوش، الصديق صالح سعيد محمد ، علي عبدالحفيظ الروياتي ، معمر مصباح عوينات
This work is licensed under a Creative Commons Attribution 4.0 International License.
