An Improved Intuitionistic Fuzzy C-Means for Ship Segmentation in Infrared Images

Abstract

Infrared ship segmentation is extensively applied in military fields. Due to noise and intensity inhomogeneity, the segmentation of infrared ship is a challenging task. The fuzzy c-means (FCM) clustering algorithm is widely used in image segmentation. However, traditional FCM is sensitive to noise and unable to obtain desirable segmentation results for infrared ship images. In this article, a novel probability induced intuitionistic FCM clustering algorithm is proposed to address the problem. First, the target probability information is incorporated into intuitionistic FCM to induce and refine membership which is affected by interferences. Second, by making use of neighborhood information in the form of a regularization term, the proposed method could suppress intensity inhomogeneity as well as maintain image details. Experimental results demonstrate that the proposed method could achieve better results than 12 other comparing algorithms for infrared ship segmentation.