Image Segmentation Research Based on Genetic Algorithm

Abstract:

As one of the difficulties and hot of computer vision and image processing, Image segmentation is highly valued by the research workers. Yet there is no image segmentation algorithm which is generic, and it is difficult to obtain an optimal feature representation method. In this paper, genetic algorithm (GA) has proposed to segment the image. GA algorithm can improve the efficiency and quality of the picture some extent through the experimental results. The algorithm has some versatility, as long as the corresponding parameters are adjusted, it can also handle the other images. The results show that GA algorithm is very stable, and the fusion result is more satisfactory. Thus, GA can be applied in image segmentation and this algorithm will have good prospects in image processing.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 1622-1625
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.1622
• Online since: November 2011
• Authors: Xiao Wei Guan, Xia Zhu
• Keywords: Genetic Algorithm (GA), Image Segmentation

References

[1] M. Awad, K. Chehdi, A. Nasri, Multi-component Image Segmentation Using Genetic Algorithms and Artificial Neural Network, IEEE Geosciences and Remote Sensing Letters, vol. 4, no. 4, pp.571-575, (2007).

DOI: 10.1109/lgrs.2007.903064

[2] J. Baker, Reducing Bias and Inefficiency in the Selection Algorithm, In Proc. of the Second International Conference on Genetic Algorithms and their Application, USA, 1987, p.1421.

[3] Wen-Xiong Kang, Qing-Qiang Yang, Run-Peng Liang, The Comparative Research on Image Segmentation Algorithms, First International Workshop on Education Technology and Computer Science, pp.703-707, , vol. 2, (2009).

DOI: 10.1109/etcs.2009.417

[4] Jie Yanga, Ran Yanga, Shigao Lib, S. Shoujing Yina, Qianqing Qina, A Nove Edge-Detection Based Segmentation Algorithm for Polarimetric Sar Images, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Pp. 141-144, VoL37. (2008).

[5] Q. Liu, Y. D. MA, Automated image segmentation using improved PCNN model based on cross-enrtopy, Journal of Image and Graphics, vol. 10, no. 5, pp.579-584, (2005).

[6] N. Otsu, A threshold selection method from gray-level histograms, IEEE Trans on Systems, Man, and Cybernetisc, vol. 9, no. 1, pp.62-66, 979.

DOI: 10.1109/tsmc.1979.4310076