Optimization of Image Features using Artificial Bee Colony Algorithm and Multi-layered Perceptron Neural Network for Texture Classification


  • Muhammad Suzuri Hitam School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia.
  • Fthi M. Albkosh School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia.
  • Wan Nural Jawahir Hj Wan Yussof School of Informatics and Applied Mathematics, Universiti Malaysia Terengganu, 21030 Kuala Nerus, Terengganu, Malaysia.


Artificial Bee Colony Algorithm, Multi-layer Perceptron, Neural Network, Texture Classification,


One of the fundamental issues in texture classification is the suitable selection combination of input parameters for the classifier. Most researchers used trial and observation approach in selecting the suitable combination of input parameters. Thus it leads to tedious and time consuming experimentation. This paper presents an automated method for the selection of a suitable combination of input parameters for gray level texture image classification. The Artificial Bee Colony (ABC) algorithm is used to automatically select a suitable combination of angle and distance value setting in the Gray Level Co-occurrence (GLCM) matrix feature extraction method. With this setting, 13 Haralick texture features were fed into Multi-layer Perceptron Neural Network classifier. To test the performance of the proposed method, a University of Maryland, College Park texture image database (UMD Database) is employed. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for GLCM which leads to the best classification accuracy performance of binary texture image classification.


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How to Cite

Hitam, M. S., Albkosh, F. M., & Hj Wan Yussof, W. N. J. (2017). 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), 9(3-4), 201–206. Retrieved from https://jtec.utem.edu.my/jtec/article/view/2942

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