Image segmentation based on a hybrid Immune Memetic Algorithm

A novel clustering algorithm called Immune Memetic Clustering Algorithm (IMCA) is proposed in the paper. IMCA combines Immune Clone Selection and Memetic algorithm; Two populations are used in the evolutionary process. Clone reproduction and selection, Memetic mutation, crossover, individual learning and selection are adopted to evolve the two populations. After watershed proceeding, extracting the texture features of an image and encoding them with real numbers, IMCA is used to partition these features, and the final segmentation result is obtained. This approach is applied to segment three types of images, including artificial synthetic texture images, natural images, and SAR images, the experimental results show the effectiveness of the proposed algorithm.