A modified fuzzy clustering algorithm based on dynamic relatedness model for image segmentation

Accurate segmentation is the basis of object detection, computer vision and other fields. However, the complexity of images, together with the existence of noise and other image artifacts, makes image segmentation still a bottleneck. In this paper, a dynamic relatedness model is presented and an improved fuzzy clustering algorithm is proposed. Compared with traditional algorithms, the relatedness model is measured in the process of image segmentation, and can avoid the effect of inaccurate features in noisy images. With the help of the proposed relatedness, more accurate information can be adopted to enhance the results. Simulated experiments on various images demonstrate that the proposed algorithm can achieve satisfying results and is insensitive to noise of different types.