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An image segmentation approach based on ant colony algorithm

Ant colony algorithm is a discrete, parallel, robustness evolutionary method which possesses the ability of fuzzy clustering. The ant colony algorithm is improved in this paper, and the algorithm starts from the perspective of clustering, integrate grayscale, gradient, neighborhood average and other characteristics of pixel for feature segmentation. In this paper, the initial clustering centers are set by two-dimensional histogram, Laplacian operator is used to divide the clustering center into two types of background and border points, and the pheromone update strategy is adopted to avoid premature convergence and stagnation phenomenon. Experiments show that the improved ant colony algorithm can quickly and accurately segment the background and borders to achieve the desired results.