1794-NM1

1794-NM1

1794-NM1

针对这一情形，N-cut 则提出了一种考虑全局信息的方法来进行图划分（Graph partitioning），即，将两个分割部分 A,B , 与全图节点的连接权重（assoc(A,V) 和 assoc(B,V)）考虑进去：

如此一来，在离群点划分中，中的某一项会接近 1，而这样的图划分显然不能使得是一个较小的值，故达到考虑全局信息而摒弃划分离群点的目的。这样的操作类似于机器学习中特征的规范化（Normalization）操作，故称为Normalized cut。N-cut不仅可以处理二类语义分割，而且将二分图扩展为 K 路（ -way）图划分即可完成多语义的图像语义分割，如下图例。

1794-NM1 

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