Work place: School of computer science, Hubei University of Technology, Wuhan, China
Research Interests: Machine Learning, Pattern Recognition, Swarm Intelligence, Image Processing
Zhiwei Ye was born on May 17, 1978 in Wuhan, Hubei. He received the B.S. and Ph.D. degrees from Wuhan University, Wuhan, China, in 2001 and 2006, respectively. Since 2016, he has been a Professor with the School of Computer Science, Hubei University of Technology, Wuhan, China. His major research interests include image processing, pattern recognition, swarm intelligence, and machine learning.
DOI: https://doi.org/10.5815/ijisa.2018.05.05, Pub. Date: 8 May 2018
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.[...] Read more.
DOI: https://doi.org/10.5815/ijisa.2015.05.02, Pub. Date: 8 Apr. 2015
Image segmentation is a basic work in the field of image analysis and computer vision. Thresholding is one of the simplest methods of image segmentation. In general, thresholding approaches based on 1-D histogram do not make use of any space adjacent information of the image, thus it is often ruined by noise; thus, thresholding methods based on 2-D histogram are put forward. These methods have better segmentation performance, but heavy computation is required with these methods. In the paper, to improve the running efficiency of thresholding methods based 2D histogram, ant colony optimization algorithm combined with genetic algorithm are employed to speed up these methods, which view 2-D histogram based thresholding as a kind of optimization problem. The proposed method has been conducted on some images. Experiments results display that the proposed approach is able to achieve improved search performance which is an efficient method and suitable for real time applications.[...] Read more.
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