科研之友
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摘要
Fuzzy C-means (FCM) clustering method has been widely used in image segmentation that plays an important role in a variety of applications in image processing and computer vision systems, but the performance of FCM heavily relies on the initial cluster centers which are difficult to determine. To solve the problem, the paper proposes a new hybrid method for image segmentation, which first randomly generates a population of initial clustering solutions and then uses an evolutionary algorithm to search for better clustering solutions; at each iteration, the FCM method is applied on each initial clustering solution to produce its segmentation result. Among a set of popular evolutionary algorithms, we find that the biogeography-based optimization (BBO) metaheuristic exhibits good performance on the considered problem. Besides the basic BBO, we have also proposed a set of improved BBO versions in their combination with FCM for image segmentation. Computational experiments on a set of test images show that the proposed method has significant advantage over the basic FCM algorithm and those hybrid algorithms combining FCM with other evolutionary algorithms such as artificial bee colony and particle swarm optimization.
