基于图的加权核K均值的图像多尺度分割

提出改进的最小割(IMC)模型以避免分割出小的孤立点集,研究了改进的最小割模型与加权核K均值之间的等价关系,列举了几种常见的用于建立图割模型边权值的相似度函数,并分析了其对分割结果的影响。在此基础上,设计了一个基于图的加权核K均值图像多尺度分割方法,该方法既避免了基于图割的图像分割中图谱的求解问题,又避免了加权核K均值方法中核矩阵的选取问题,同时实现了对图像多尺度的分割。通过对该方法进行抗噪性能的分析,以及在光学图像上对实验结果进行比较,验证了所提出方法的有效性。

Abstract

An improved minimum cut model is presented considering that the minimum cut criteria favors cutting small sets of isolated nodes,then equivalence relation between the improved minimum cut model and weighted kernel k-means is researched,and the influence of different similarity functions on the results of segmentation are also analysed. And based on these,a multiscale image segmentation method based on graph weighted kernel k-means is proposed,this method avoids calculating graph spectral,which is a key step when using graph cut model to segment images,also,it avoids selecting kernel matrix,which is important to the weighted kernel k-means,finally it realizes multiscale image segmentation. The analysis of anti-noise and experimental results on a number of optical images show the effectiveness of this method.

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李昱川, 田铮. 基于图的加权核K均值的图像多尺度分割[J]. 光学学报, 2009, 29(10): 2762. Li Yuchuan, Tian Zheng. Multiscale Image Segmentation Based on Graph Weighted Kernel K-means[J]. Acta Optica Sinica, 2009, 29(10): 2762.

基于图的加权核K均值的图像多尺度分割

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