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Fthi M. Albkosh
Univesiti Malyisa Terengganu
Computer Science & IT Electrical & Electronics Engineering

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Optimization of discrete wavelet transform features using artificial bee colony algorithm for texture image classification Fthi M. Albkosh; Muhammad Suzuri Hitam; Wan Nural Jawahir Hj Wan Yussof; Abdul Aziz K Abdul Hamid; Rozniza Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 6: December 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (921.614 KB) | DOI: 10.11591/ijece.v9i6.pp5253-5262

Abstract

Selection of appropriate image texture properties is one of the major issues in texture classification. This paper presents an optimization technique for automatic selection of multi-scale discrete wavelet transform features using artificial bee colony algorithm for robust texture classification performance. In this paper, an artificial bee colony algorithm has been used to find the best combination of wavelet filters with the correct number of decomposition level in the discrete wavelet transform.  The multi-layered perceptron neural network is employed as an image texture classifier.  The proposed method tested on a high-resolution database of UMD texture. The texture classification results show that the proposed method could provide an automated approach for finding the best input parameters combination setting for discrete wavelet transform features that lead to the best classification accuracy performance.
Co-Authors Abdul Aziz K Abdul Hamid Muhammad Suzuri Hitam Rozniza Ali Wan Nural Jawahir Hj Wan Yussof