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.