Application of Bat Algorithm for Texture Image Classification

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Zhiwei Ye 1,2,* Xiangfeng Hou 3 Xu Zhang 2 Juan Yang 2

1. Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing, China

2. School of Computer Science, Hubei University of Technology, Wuhan, China

3. College of Physics and Electronic Science, Hubei Normal University, Huangshi, China

* Corresponding author.


Received: 21 Jul. 2017 / Revised: 5 Sep. 2017 / Accepted: 26 Oct. 2017 / Published: 8 May 2018

Index Terms

Image classification, Texture analysis, Tuned mask, Feature extraction, Bat algorithm


Textural feature extraction of image is a basic work for image analysis. A number of approaches have been put forward to describe texture features quantitatively, such as gray level co-occurrence matrix, fractal wavelet, Gabor wavelet and local binary pattern etc, among them texture feature extracted based on “tuned” mask will not suffer from rotation and scale of images. However, it needs to take a lot of time to learn the tuned mask with the traditional methods and could not acquire the satisfying mask sometimes. In essence, it is a very hard combinational optimization problem and easy to fall into the local optimum with mountain climbing method. Bat algorithm is a newly proposed meta-heuristic optimization, which is employed to tune the optimal mask in the paper. The procedure of bat algorithm to learn the tuned mask is detailed. Experiments results testifies that the proposed method is propitious to draw texture features, its performance is better than the simple particle swarm optimization and genetic algorithm based mask tuning scheme, which is a robust approach for texture image analysis.

Cite This Paper

Zhiwei Ye, Xiangfeng Hou, Xu Zhang, Juan Yang, "Application of Bat Algorithm for Texture Image Classification", International Journal of Intelligent Systems and Applications(IJISA), Vol.10, No.5, pp.42-50, 2018. DOI:10.5815/ijisa.2018.05.05


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