An efficient evolutionary image segmentation algorithm

In this paper, an efficient evolutionary image segmentation algorithm (EISA) is proposed. The existing evolutionary approach of image segmentation has the advantages over the other approaches such as continuous contour, non-oversegmentation, and non-thresholds, but suffers from long computation time. EISA uses a K-means algorithm to split an image into many homogeneous regions and then merges the split regions automatically using an evolutionary algorithm. The image segmentation problem is formulated as an optimization problem and the objective function is also given. EISA using a novel chromosome encoding method and a novel intelligent genetic algorithm makes the segmentation results be robust and the computation time be much shorter than the existing evolutionary image segmentation algorithms. Design and analysis of EISA are also presented. Experimental results of natural images with various degrees of noise demonstrate the effectiveness of EISA.