Image Segmentation using Improved Imperialist Competitive Algorithm and a Simple Post-processing

Image segmentation is a fundamental step in many image processing applications. In most cases the image pixels are clustered only based upon the pixels’ intensity or color information, and neither spatial nor neighborhood information of pixels is used in the clustering process. Considering the importance of including spatial information of pixels which improves the quality of image segmentation, and using the information of neighboring pixels, cause the accuracy of segmentation to be enhanced. In this paper the idea of combining the K-means algorithm and the improved imperialist competitive algorithm is proposed. Also before applying the hybrid algorithm, a new image is created and then the hybrid algorithm is employed. Finally, a simple post-processing is applied to the clustered image. Comparing the results of applying the proposed method to different images with other methods shows that in most cases, the accuracy of non-local imperialistic competitive algorithm (NLICA algorithm) is better than the other methods