Gray image segmentation based on fuzzy c-means and artificial bee colony optimization

Abstract

The regions obtained by image segmentation need to satisfy both the requirements of uniformity and connectivity. Image segmentation is the process of dividing an image into several specific regions. The result of image segmentation is a set of combinations covering the main feature areas of the whole image. The pixels in an area are similar to some or calculated characteristics, but there are obvious differences between adjacent areas. In this paper, a gray image segmentation algorithm based on fuzzy C-means combined with bee colony algorithm is proposed, which has strong optimization ability for multi-objective problems. By using the fuzzy membership function of the fuzzy C-means algorithm, the optimal clustering centers in the artificial bee colony optimization algorithm can be quickly calculated. It makes image segmentation faster and more accurate. The bee colony search algorithm is optimized and an effective local search algorithm is designed, it makes the bee colony converge to the optimal solution efficiently. Finally, the improved fuzzy C-means and artificial bee colony optimization algorithm are used to improve and optimize the seed region growth method. The multi-criteria are taken as the multi-objective optimization problem, and the segmentation results are finally obtained. Benefiting from our local search program and feature extraction in multi-color space, it makes the stability; efficiency and accuracy of image segmentation are higher.