提出了一种改进的信息割（MIC）算法。首先证明了信息割（IC）模型与Cauchy-Schwarz cut（CScut）等价，并通过图谱方法给出IC目标函数优化问题的最优解；其次利用图像中像素点间的灰度和空间关联性，在IC算法的基础上提出一种MIC算法，该算法首次使用联合灰度信息和空间位置信息的Parzen窗函数来估计概率密度函数，降低了图像中灰度变化对图像分割的影响。加噪合成图像及遥感图像分割实验结果表明MIC算法较IC算法具有更好的抗噪性能，且与图谱方法相比计算复杂度显著降低。

A modified information cut algorithm(MIC) is presented. First, information cut(IC) model is demonstrated to be equivalent to Cauchy-Schwarz cut(CScut), and then the optimal solution of IC objective function using graph spectral method is proposed; Using both the gray and space relationship of pixels in an image, a MIC algorithm is proposed based on IC algorithm, this method firstly utilizes Parzen windowing function that combines gray information and space information to evaluate probability density functions, and thus reduces the effect of gray changes to image segmentation. Experiments using synthetic image with noise and remote sensing images indicate that MIC algorithm has better anti-noise performance than IC algorithm, and lower computational complexity compared with graph spectral methods.