说明：  提出了一种新颖的处理图像视觉聚类问题的方法。与以往关注图像局部特征和局部连续性的方法不同，本文中的方法能够提取关于图像的全局印象。为此，我们将图像分割问题转化为图划分问题并提出了划分中的一种全局判别准则——Ncut (Normalized Cut)。Ncut不仅能够衡量不同聚类之间的相异程度，还能够衡量各聚类内部的相似程度。为求解Ncut 的最优化问题，提出了一种基于广义特征值问题的高效算法，并将此算法应用于静态图像分割，取得了良好的效果。
(proposed a novel image processing visual clustering problem. Concerned with the past, image characteristics and continuity of the partial difference in the way this method can extract images on the overall impression. To this end, We will image segmentation into the map division and the division of a global criterion-- Ncut ( Normalized Cut). Ncut different not only to measure the difference between the cluster level, but also to measure the internal cluster degree of similarity. Ncut to solve the optimization problem, based on a generalized eigenvalue problem of efficient algorithms, this algorithm will be applied to the static image segmentation, and achieved good results.)