A Simple and Fast GA-SA hybrid Image Segmentation Algorithm
نویسندگان
چکیده
Image segmentation is an important process of image analysis. Most of the published approaches for image segmentation need to set appropriate parameter values to cope with the uncertainty problem. However, the parameter values are usually problem dependent and hardly obtained. In this paper, a simple and fast GA-SA hybrid image segmentation algorithm (HISA) is proposed. In HISA, the well-known K-means algorithm is used to split an image into many small regions first. Then, the genetic algorithm is applied to search a good or usable region segmentation, which maximizes the quality of regions that generated by split-and-merge processing. The simulated annealing algorithm (SA) is combined with a genetic algorithm (GA) to reduce the length of chromosomes for improving the convergence speed. The proposed algorithm HISA can lead to better computational efficiency and higher segmentation accuracy. Experimental results using several natural images to demonstrate the feasibility and the efficiency of the proposed algorithm.
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