基于FCM和离散正则化的多目标图像分割

摘要

针对机器视觉中的多目标图像分割问题，提出一种适用于多目标物体的图像分割算法。首先对图像进行图像增强预处理；然后采用基于直方图的模糊 C 均值聚类算法完成分类任务，实现图像的初分割，将分类后的像素作为种子集；最后利用离散正则化的半监督方法得到自动修正分类结果。实验结果表明，与已有的多目标分割算法相比，该算法分割结果更加精确。%To address the problem of multi-objective image segmentation in computer vision, a multi-objec-tive image segmentation method based on fuzzy C-Means and discrete regularization is proposed in this pa-per. First, the method preprocesses an input image with image enhancement. Secondly, the FCM clustering algorithm based on histogram is used to classify the pixels in the images into the different categories and realize the initial segmentations. Finally a discrete regularization algorithm as a semi-supervised method re-vises the classified results. The experiments demonstrate the superior performance of the proposed method in terms of segmentation accuracy.