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Optimizing FCM for segmentation of image using Gbest-guided artificial bee colony algorithm

Traditional fuzzy C-Means (FCM) clustering algorithm is sensitive to the initial value and converges to a local minimum easily that may lead to produce poor phenomena of segmenting image. This paper uses improved artificial bee colony to optimize FCM algorithm and uses improved methods to segment image. The improved algorithm (GABCFCM) uses Gbest-guided artificial bee colony (GABC) algorithm which searches the global best solution strongly, and it uses FCM which has ability to cluster data. Clustering segmentation of gray image is realized in this paper. Experiment results show that GABCFCM algorithm does not increase the consumption of time obviously and it improves the speed of converging to the global best solution by comparing with the ABCFCM which uses traditional artificial bee colony algorithm to optimize FCM. GABCFCM improved the reliability of image segmentation by using FCM algorithm greatly. GABCFCM can be applied to image processing of requiring higher relative velocity in the same number of cycles. This paper also uses some steps to improve PSABCFCM and PSABCLUTFCM and uses them in the fields of image segmentation.