Infrared Image Segmentation using Adaptive FCM Algorithm Based on Potential Function

Abstract

Traditional Fuzzy C-means segmentation algorithm requires to set clustering number in advance, and to calculate image clustering center by the iterative arithmetic. So the traditional algorithm is sensitive to the initial value and the computation complexity is high. In order to improve the traditional Fuzzy C-means algorithm, this paper presents an infrared image segmentation method using adaptive Fuzzy C-means algorithm based on potential function. The presented algorithm can directly determine the optimal clustering number and clustering center for infrared image to be segmented by the potential function. After calculating the membership matrix of pixels in the infrared image by the fuzzy theory, the final segmented image is obtained through the fuzzy clustering. The experiments show that the presented algorithm in the paper could determine the optimal clustering number of the infrared image adaptively, and ensure the accuracy of segmentation, while significantly reducing the computation speed and complexity of the algorithm.