Image Segmentation Based on K-means and Genetic Algorithms

Abstract

In this paper, we studied image segmentation to which we applied a combination of the genetic algorithm and the cooperation between unsupervised classification by K-means and contour detection by the Sobel filter to improve image segmentation results by the K-means method alone. First, we will apply the segmentation process by combining two methods in the following way: We have hybridized the K-means method which is used to classify pixels into classes (regions), with the Sobel gradient filter which will then detect the edges of these regions, and then we will apply the genetic algorithm and by scanning further in the response space, try to find better quality class centers. This process is withdrawn until they are unable to find two sufficiently similar neighboring regions. The effectiveness of the proposed method was studied on a number of images. It is also compared by the K-means algorithm.