IEEE Trans Image Process. 2013 Mar;22(3):1258-61. doi: 10.1109/TIP.2012.2226048. Epub 2012 Oct 22.
In a recent paper, Krinidis and Chatzis proposed a variation of fuzzy c-means algorithm for image clustering. The local spatial and gray-level information are incorporated in a fuzzy way through an energy function. The local minimizers of the designed energy function to obtain the fuzzy membership of each pixel and cluster centers are proposed. In this paper, it is shown that the local minimizers of Krinidis and Chatzis to obtain the fuzzy membership and the cluster centers in an iterative manner are not exclusively solutions for true local minimizers of their designed energy function. Thus, the local minimizers of Krinidis and Chatzis do not converge to the correct local minima of the designed energy function not because of tackling to the local minima, but because of the design of energy function.
在最近的一篇论文中，Krinidis 和 Chatzis 提出了一种用于图像聚类的模糊 c-均值算法的变体。通过能量函数以模糊的方式合并局部空间和灰度信息。提出了设计的能量函数的局部极小值，以获得每个像素和聚类中心的模糊隶属度。在本文中，证明了 Krinidis 和 Chatzis 的局部极小值以迭代方式获得模糊隶属度和聚类中心不是其设计能量函数的真正局部极小值的唯一解。因此，Krinidis 和 Chatzis 的局部极小值不会收敛到设计能量函数的正确局部最小值，不是因为处理局部最小值，而是因为能量函数的设计。
