基于图划分的图像直方图聚类分割

Image segmentation by graph partition on histogram clustering

传统的基于图论的图像分割方法都是直接对图像灰度数据进行聚类分割,算法计算量较大。提出一种新的基于图论的直方图聚类分割算法,算法对图像直方图数据进行聚类,并由此得到分割阈值。由于输入值为直方图数据而不是图像灰度,数据量最大为 256而与像素数无关。实验研究表明,本方法在分割质量基本不变的情况下使得计算量大为减少。

In traditional graph theory based image segmentation methods,the grayscale value of an image is processed directly to obtain clustering results, but the computing time of these methods is very large. A novel segmentation method based on graph partition on histogram clustering was presented. The proposed algorithm obtained threshold by clustering histogram potential function. Since the input is histogram data, the computation time will not be affected by the image size. Experiment results demonstrate that the computation time can be significantly reduced by the proposed algorithm.