摘要
目前一些基于模糊核聚类的图像分割方法得到了大量研究,但难以有效地解决核方法中的参数合理选择问题,分割结果受到核参数人为主观选择的制约,不能达到分割的自适应性和良好性.通过提出一种基于成对约束的混合高斯核的方法来解决上述问题.将传统的高斯核函数改进为混合核函数,该混合核函数由多个不同核参数的高斯核函数组成,对于该混合核函数采用基于成对约束的类别信息算法求解其中的核参数和权重系数,进而采用该混合核函数对图像进行聚类分割.实验结果表明:该方法成功解决了模糊核聚类中核参数的选择问题,使得聚类更具有自适应性,而且由该混合核参数得到的图像分割结果更为鲁棒和准确.
Now lots of researches on fuzzy kernel clustering for segmentation have been done. But the selection of the kernel parameter is not solved effectively. The result of segmentation is influenced by the kernel parameter,so that the segmentation is not adaptive and well. The use of hybrid kernels with pairwise constraints is proposed to the above problems. A composite gaussian kernel,which is composited by many gaussian kernels with different kernel parameter,is used to replace the traditional gaussian kernel. The information about classifications with pairwise constraints is to optimize the parameters of such a composite gaussian kernel. Then the composite gaussian kernel is used to cluster for segmentation. The experimental results demonstrate that this technique can solve the problem of selection of kernel parameter successfully and make the segmentation adaptively. The results of segmentation are robust and accurate using the hybrid kernels.
出处
《微电子学与计算机》
CSCD
北大核心
2010年第5期177-180,184,共5页
Microelectronics & Computer
关键词
图像分割
模糊核聚类
混合核函数
成对约束
image segmentation
fuzzy kernel clustering
hybrid kernels
pairwise constraints
