The variants of the harmony search algorithm: an overview
Artificial Intelligence Review
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Image segmentation is considered as one of the crucial steps in image analysis process and it is the most challenging task. Image segmentation can be modeled as a clustering problem. Therefore, clustering algorithms have been applied successfully in image segmentation problems. Fuzzy c-mean (FCM) algorithm is considered as one of the most popular clustering algorithm. Even that, FCM can generate a local optimal solution. In this paper we propose a novel Harmony Fuzzy Image Segmentation Algorithm (HFISA) which is based on Harmony Search (HS) algorithm. A model of HS which uses fuzzy memberships of image pixels to a predefined number of clusters as decision variables, rather than centroids of clusters, is implemented to achieve better image segmentation results and at the same time, avoid local optima problem. The proposed algorithm is applied onto six different types of images. The experiment results show the efficiency of the proposed algorithm compared to the fuzzy c-means algorithm.