针对现有的基于归一化的图谱理论阈值分割算法的权值计算公式没有充分考虑像素点的关联，在图像含有弱边界时很难得到真实解，导致图像细节分割不理想的问题，本算法首先使用高斯混合模型构造新的约束条件引入到权值计算中，使得权值计算充分地考虑像素点之间的关联。在计算图谱划分测度前，本算法通过高斯混合模型的均值参数自适应确定门限值的分布区间，较大地提高了图谱划分测度计算的效率。实验结果表明，相对于现有的基于归一化的图谱理论的阈值分割方法，本文算法具有较好的分割效果，可以保留图像更多细节。

Weight calculating formulas of existing threshold segmentation algorithms based on graph spectral theory via normalized cut do not pay enough attention to the relationship between pixels, can not get the real solution when images have weak edges and thus cannot segment the details of images very well.The proposed algorithm pay enough attention to the relationship between pixels when calculate weight by introducing a new constraint which is made by Gaussian Mixture Model to the algorithm. Before computing normalized graph cut measure, proposed algorithm computes the distribution of threshold range adaptively by the median parameter of Gaussian Mixture Model, therefore the proposed algorithm makes the computation of normalized graph cut measure very efficient. Experiments show that our algorithm performs better in segmentation and preserve more details than existing threshold segmentation algorithms based on graph spectral theory via normalized graph cut measure.