Image Segmentation Based on Genetic Algorithms Combination
The paper describes a new image segmentation algorithm called Combined Genetic segmentation which is based on a genetic algorithm. Here, the segmentation is considered as a clustering of pixels and a similarity function based on spatial and intensity pixel features is used. The proposed methodology starts from the assumption that an image segmentation problem can be treated as a Global Optimization Problem. The results of the image segmentations algorithm has been compared with recent existing techniques. Several experiments, performed on real images, show good performances of our approach compared to other existing methods.
- 1.Jain, A.K., Flynn, P.: Image Segmentation Using Clustering. In: Bowyer, K., Ahuja, N. (eds.) Advances In Image Understanding: A Festschrift for Azriel Rosenfeld, pp. 65–83. IEEE Computer Society Press, Los Alamitos (1996)Google Scholar
- 3.Lo Bosco, G.: A genetic algorithm for image segmentation. In: Proc. of ICIAP 2001, Palermo, Italy, pp. 262–266. IEEE computer society press, Los Alamitos (2001)Google Scholar
- 6.Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 7.Goldberg, D.E.: Genetic algorithms in search, optimization and machine leraring. Addison-Wesley, Reading (1989)Google Scholar
- 9.R.S.I.S., EPRI TR-11838, WO-5144-03 & WO-8632-01, Palo Alto (October 1999)Google Scholar
- 10.Martin, D., Fowlkes, C., Tal, D., Malik, J.: A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms. In: Proc. of ICCV 2001, vol. 2, pp. 416–425. IEEE Computer Society press, Los Alamitos (2001)Google Scholar