基于正则割(Ncut)的多阈值图像分割方法

摘要

The thresholding is an important form of image segmentation and is used in many applications that involve image processing and object recognition. Thus, how to acquire a threshold of image segmentation is crucial. A novel multilevel thresholding algorithm is presented, which regards gray level L and corresponding probability P of ID histogram of the image as points (L, P) in two-dimensional plane, and uses a new similarity function to define the similarity between any two points to construct the similarity matrix based on gray level, then uses the spectral clustering algorithm (Ncut) to classify the points, and the image thresholding is determined by the classification result. The similarity matrices are based on the gray levels of an image, rather than the commonly used image pixels. Therefore, the proposed algorithm occupies much smaller storage space and requires much lower computational costs. At the same time, this algorithm has the superior performance that is single-threshold and multi-threshold for image segmentation, compared to existing thresholding algorithms.%在图像处理与目标识别中广为应用的阈值法是图像分割的一种重要方法,因此如何确定阈值是图像分割的关键.提出了一种新的图像阈值分割方法,把图像的一维灰度直方图的灰度级L和对应灰度级L的概率P视为二维平面上的点(L,P),采用新的相似度函数来定义这些点之间的相似度,从而构建基于灰度级的相似度矩阵,然后使用正则割(Ncut)进行分类,根据分类结果确定图像的分割阈值.算法用基于灰度级的权值矩阵代替基于像素级的权值矩阵来描述图像像素的关系,因而需要的存储空间及实现的复杂性大大减少；与现有的阈值分割方法相比,该算法能够单阈值和多阈值分割图像,因此具有更为优越的性能.