基于模糊连接度的近邻传播聚类图像分割方法

针对现有近邻传播聚类图像分割方法分割精度低的问题,提出一种基于模糊连接度的邻近传播聚类(FCAP)图像分割算法。针对传统模糊连接度算法不能得出任意点对间模糊连接度的不足,结合最大生成树提出了全模糊连接度算法。FCAP算法先使用Normalized Cut超像素技术进行超像素分割，这些超像素可以看作数据点以及它们之间的模糊连接度;然后使用所提出的全模糊连接度算法计算超像素间的模糊连接度,根据模糊连接度和空间信息计算超像素的相似度;最后使用近邻传播(AP)聚类算法完成分割。实验结果表明,FCAP算法明显优于超像素处理后直接使用AP聚类算法进行分割的方法,并且优于无监督图像分割方法。

Segmentation method for affinity propagation clustering images based on fuzzy connectedness

Considering the low accuracy of the existing image segmentation method based on affinity propagation clustering, a FCAP algorithm which combined fuzzy connectedness and affinity propagation clustering was proposed. A Whole Fuzzy Connectedness (WFC) algorithm was also proposed with concerning the shortcoming of traditional fuzzy connectedness algorithms that can not get fuzzy connectedness of every pair of pixels. In FCAP, the image was segmented by using super pixel technique. These super pixels could be considered as data points and their fuzzy connectedness could be computed by WFC. Affinities between super pixels could be calculated based on their fuzzy connectedness and spatial distances. Finally, affinity propagation clustering algorithm was used to complete the segmentation. The experimental results show that FCAP is much better than the methods which use affinity propagation clustering directly after getting super pixels, and can achieve competitive performance when comparing with other unsupervised segmentation methods.