Image segmentation based on edge detection using K-means and an improved ant colony optimization

On the base of edge detection using the K-means algorithm and an improved ant colony optimization (ACO), a novel image segmentation algorithm is proposed. The proposed method can enhance advantages and avoid disadvantages of edge-based and clustering methods by embedding the clustering in edge detection, combining them in a novel way. Since the clustering centers are determined roughly using K-means in ACO, the proposed algorithm can address the problem of slow convergence of the traditional ant colony (AC) algorithm and reduces its complexity. Experiments are divided into two stages to test the results of edge detection and segmentation respectively. It is shown that the proposed algorithm based on superior edge detection achieves better performance compared to the typical image segmentation methods.