Simple and Efficient Clustering Approach Based on Cuckoo Search Algorithm

Image segmentation is the most important operation in the image processing system because it is located at the articulation between image processing and analysis. The advantage of segmentation is to partition an image into several homogeneous regions, within the meaning of a criterion fixed a priori. A multitude of segmentation methods are proposed in the literature, but there is no universal image segmentation technique to apply to all different types of images and in any given computer context. Because of these constraints, in this paper, we will propose a new image segmentation approach which is based on the hybridization of an unsupervised classification method which is fuzzy C-means (FCM) and a metaheuristic which is called Cuckoo Search Algorithm (CSA). In our proposed approach, the cuckoo search is used to find the optimal partitioning according to an objective function which is based on the indices of validity of the clusters. First, CSA is initialized with random cluster centers. The cluster centers are then updated using the CSA principles aimed at minimizing the objective function proposed. The performance of the proposed approach was measured on several images and compared to other existing FCM techniques such as standard FCM and FCM based on genetic algorithms (FCM-GA). The experimental results show that the proposed approach yields satisfactory results in terms of precision, simplicity and efficiency.