（1）应用9×9的窗口对上述图象进行随机抽样，共抽样200块子图象；

（2）将所有子图象按列相接变成一个81维的行向量；

（3）对所有200个行向量进行KL变换，求出其对应的协方差矩阵的特征向量和特征值，按降序排列特征值以及所对应的特征向量；

（4）选择前40个最大特征值所对应的特征向量作为主元，将原图象块向这40个特征向量上投影，所获得的投影系数就是这个子块的特征向量。

（5）求出所有子块的特征向量。

-(1) the application of 9 × 9 window of these images at random, a total sample of 200 sub-image　(2) all sub-image by out-phase into a 81-dimensional vector lines　(3) All 200 line vector KL transform, derive its corresponding covariance matrix eigenvectors and eigenvalues, in descending order eigenvalues and corresponding eigenvectors　(4) a choice to 40 corresponding to the largest eigenvalue eigenvector as the main element, the original image block to the 40 on the projection eigenvector obtained projection coefficient is the sub-block eigenvector. (5) calculated for all sub-block eigenvector.