Research of image segmentation based on graph theory and minimum cut set algorithm
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摘要: 为了提高图像分割的质量,采用图论最小割集算法进行了研究。首先将图像中的像素点映射为图论节点,节点权值通过平衡因子与共享最近邻节点数的比率计算;然后基于最小化能量方程建立图像最小割集,提取分割块内的灰度值作为块特征向量,用最小生成树对图分割;接着用判定函数判断临近区域是合并或者分割;最后给出了算法流程。结果表明,该算法可以分割出目标信息,并且算法鲁棒性好、峰值内存小。Abstract: In order to improve the quality of image segmentation, graph theory and minimal cut set algorithm were used. Firstly, using the pixel points of image as the mapping nodes of the graph theory, the node weight were calculated by the ratio of the balance factor and the shared nearest neighbor nodes. Then, the minimum cut set of the image was established based on the minimized energy equation, the gray value of the segmentation block was extracted as the block feature vector and the image was segmented by minimum spanning tree. The adjacent regions were judged to be combined or to be segmented by judging function. Finally the algorithm flow was given. The results show that the target information can be segmented by this algorithm. This algorithm has good robustness and small peak memory.
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Keywords:
- image process /
- minimum cut set /
- weight /
- minimum spanning tree
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