An Image Thresholding Approach Based on Ant Colony Optimization Algorithm Combined with Genetic Algorithm

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Zhiwei Ye 1,* MingWei Wang 1 Huazhong Jin 1 Wei Liu 1 XuDong Lai 2

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

2. School of remote sensing and information Engineering, Wuhan University, Wuhan, China

* Corresponding author.


Received: 10 Aug. 2014 / Revised: 10 Nov. 2014 / Accepted: 27 Jan. 2015 / Published: 8 Apr. 2015

Index Terms

Image Segmentation, Image Thresholding, Optimization, 2-D Fisher Criteria, Ant Colony Optimization Algorithm, Genetic Algorithm


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.

Cite This Paper

Zhiwei Ye, MingWei Wang, Huazhong Jin, Wei Liu, XuDong Lai, "An Image Thresholding Approach Based on Ant Colony Optimization Algorithm Combined with Genetic Algorithm", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.5, pp.8-15, 2015. DOI:10.5815/ijisa.2015.05.02


[1]Gonzalez, Rafael C. & Woods, Richard E.(2002). Thresholding. In Digital Image Processing, pp. 595–611. Pearson Education
[2]Mehmet Sezgin and Bulent Sankur, Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging, 2004, 13(1): 146-165
[3]Hossein Mobahi, Shankar Rao, Allen Yang, Shankar Sastry and Yi Ma. Segmentation of Natural Images by Texture and Boundary Compression, International Journal of Computer Vision (IJCV), 2011, 95 (1): 86-98
[4]S.K Somasundaram, P.Alli, A Review on Recent Research and Implementation Methodologies on Medical Image Segmentation”, Journal of Computer Science, 2012, 8(1): 170-174
[5]Bo Peng, Lei Zhang , David Zhang, Automatic Image Segmentation by Dynamic Region Merging, IEEE Transactions on image processing, 2011, 20(12): 3592 - 3605
[6]F. Y. Shih, S. Cheng, Automatic seeded region growing for color image segmentation, Image and Vision Computing 23 (2005) 590 877–886.
[7]P. L. Palmer, H. Dabis, J. Kittler, A performance measure forboundary detection algorithms, Computer Vision and Image Understanding, 1996, 63: 476-494.
[8]Barghout, Lauren, and Jacob Sheynin. "Real-world scene perception and perceptual organization: Lessons from Computer Vision." Journal of Vision 13.9 (2013): 709-709.
[9]V. Caselles, R. Kimmel, and G. Sapiro. Geodesic active contours. International Journal of Computer Vision, 1997,22(1):61-79
[10]K J. Batenburg, and J. Sijbers, Adaptive thresholding of tomograms by projection distance minimization, Pattern Recognition,2009,42(10): pp. 2297-2305.
[11]K J. Batenburg, and J. Sijbers, Optimal Threshold Selection for Tomogram Segmentation by Projection Distance Minimization, IEEE Transactions on Medical Imaging, 2009, 28(5): 676-686
[12]S. Abutaleb. 1989. Automatic Thresholding of Gray-Level Pictures Using Two-Dimensional Entropy[J]. Comput. Vision Graphics Image Process, 47, pp.22-32
[13]Pal, N.R., Pal, S.K., 1993. A review on image segmentation techniques. Pattern Recognition, 26, pp.1277–1294.
[14]Sahoo, P., Wilkins, C., Yeager, J., 1997. Threshold selection using Renyi’s entropy. Pattern Recognition, 30, pp.71–84.
[15]L. Li, J. Gong, and W. Chen, Gray-level image thresholding based on fisher linear projection of two-dimensional histogram,’’ Pattern Recogn. 30, 743–749 1997
[16]TANG Yinggan HUANG Na, GUAN Xinping, 2009. Infrared Image Segmentation Using Two-Dimensional Fisher Linear Optimal Discriminant Analysis and Particle Swarm Optimization. Chinese Journal Of Electron Devices,32(1), pp.12-16
[17]F. Du, W. K. Shi, L. Z. Chen, et al, 2005. Infrared image segmentation with 2-D maximum entropy method based on PSO. Pattern Recognition Letters, 26, pp. 597-603
[18]Multilevel Image Thresholding Based on 2D Histogram and Maximum Tsallis Entropy— A Differential Evolution Approach. IEEE Transactions on Image Processing, 2012, Vol.22.(12): 4788 - 4797
[19]Ningbo Zhu Gang Wang ; Gaobo Yang ; Weiming Dai. A Fast 2D Otsu Thresholding Algorithm Based on Improved Histogram. Chinese Conference on Pattern Recognition 2009, IEEE press, Nanjing, China, pp.1-5
[20]Jun Zhang, ,Jinglu Hu. Image Segmentation Based on 2D Otsu Method with Histogram Analysis. 2008 International Conference on Computer Science and Software Engineering, IEEE press, pp.105-108.
[21]M. Dorigo, G.D. Caro, and L.M. Gambardella, Ant algorithmfor Discrete Optimization, Artificial Life,1999, 5, pp. 1-10.
[22]M. Dorigo, C. Blum, Ant colony optimizationtheory: A survey., Theoretical Computer Science, 2005, 344, pp. 243–278
[23]Brent, O. ; Thiruvady, D. ; Gomez-Iglesias, A. ; Garcia-Flores, R. A parallel Lagrangian-ACO heuristic for project scheduling .IEEE Congress on Evolutionary Computation (CEC), 2014:2985 - 2991
[24]Sharma, S. ; Buddhiraju, K.M. ; Banerjee, B. An ant colony optimization based inter domain cluster mapping for domain adaptation in remote sensing 2014 IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), : 2158 - 2161
[25]Ganganath, N. ; Chi-Tsun Cheng ; Tse, C.K. An ACO-based off-line path planner for nonholonomic mobile robots. 2014 IEEE International Symposium on Circuits and Systems (ISCAS), : 1038 – 1041
[26]Ye zhiwei, Zheng zhaobao, Yu Xin, Ning xiaogang. Automatic threshold selection based on ant colony optimization. 2005 ICNN&B’05, Beijing,pp.728-732
[27]Yu-feng Jiang, Juan Wang. The Model of Dam Displacement Based on Improved Ant Colony Algorithm-Neural Networks. IEEE 2009 First International Workshop on Database Technology and Applications, Wuhan, China, 25-26 April 2009, pp.337-340
[28]Guangchao Wu Han Huang, Theoretical Framework of Binary Ant Colony Optimization Algorithm. Fourth International Conference on Natural Computation , IEEE press, 2008:526-530
[29]Schmitt, Lothar M (2001), Theory of Genetic Algorithms, Theoretical Computer Science 259: 1–61
[31]XIONG Zhi-Hui, LI Si-Kun, CHEN Ji-Hua. Hardware/Software Partitioning Based on Dynamic Combination of Genetic Algorithm and Ant Algorithm. Journal of Software,2005,16(4):503-512.