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Improved image threshold method based on graph spectral theory
改进的图谱理论阈值分割方法

Keywords: graph spectral theory,image threshold,Gaussian mixture model,normalized graph cut measure
图谱理论
,阈值分割,高斯混合模型,图谱划分测度

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Abstract:

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.

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