中文说明：标准化切割算法既可以度量不同组之间的总不相似性，也可以度量组内的总相似性。令人惊奇的是，分裂点的最优解很容易通过求解一个广义特征值问题来计算。一种解决视觉中知觉分组问题的新方法。我们的方法不是关注局部特征及其在图像数据中的一致性，而是着眼于提取图像的全局印象。他们将图像分割视为一个图分割问题，提出了一种新的全局分割准则&归一化割。标准化割准则既可以度量不同组之间的总体差异性，也可以度量组内的总体相似性。结果表明，基于广义特征值问题的有效计算方法可以用来优化该准则。他们将这种方法应用于分割静态图像和运动序列
English Description：
The Normalized Cuts algorithm measures both the total dissimilarity between the different groups as well as the total similarity within the groups. Amazingly, the optimal solution of splitting points is easily computed by solving a generalized eigenvalue problem. A novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. They treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. They show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. They have applied this approach to segmenting static images, as well as motion sequences