Абстрактный

Self-adapted fuzzy C-means segmentation algorithm based on bacterial chemotaxis

Li Yanling, Li Gang

Although fuzzy c-means algorithm is one of the most popular methods for image segmentation, it is in essence a technology of searching local optimal solution and sensitive to initial data. For this, self-adapted fuzzy c-means segmentation algorithm based on bacterial chemotaxis is proposed in this paper. In the new algorithm, selfadapted fuzzy c-means algorithm is used to get the initial number of clusters and bacterial chemotaxis algorithm is used for avoiding falling into local optimization. Experimental results show that the proposed algorithm used for image segmentation can segment images more effectively and can provide more robust segmentation results.