区间模糊谱聚类图像分割方法

近年来谱聚类算法在模式识别和计算机视觉领域被广泛应用，而相似性矩阵的构造是谱聚类算法的关键步骤。针对传统谱聚类算法计算复杂度高难以应用到大规模图像分割处理的问题,提出了区间模糊谱聚类图像分割方法。该方法首先利用灰度直方图和区间模糊理论得到图像灰度间的区间模糊隶属度，然后利用该隶属度构造基于灰度的区间模糊相似性测度，最后利用该相似性测度构造相似性矩阵并通过规范切图谱划分准则对图像进行划分，得到最终的图像分割结果。由于区间模糊理论的引入，提高了传统谱聚类的分割性能，对比实验也表明该方法在分割效果和计算复杂度上都有较大的改善。

An interval fuzzy spectral clustering algorithm for image segmentation

In recent years, spectral clustering algorithms have been widely used in the field of pattern recognition and computer vision, and the construction of the similarity matrix is the key issue of spectral clustering algorithms. Due to the high computational complexity, it is hard to apply spectral clustering algorithms to large scale image segmentation. Aiming at this problem, an interval fuzzy spectral clustering algorithm for image segmentation is proposed. The method firstly uses the grayscale histogram and the interval fuzzy theory to obtain the interval fuzzy membership degree between image grayscales, then uses this membership degree to construct the grayscale based interval fuzzy similarity measure. Finally, the similarity measure is used to construct the similarity matrix and the image are grouped by normalized cut criterion so as to obtain the final image segmentation results. Due to the introduction of interval fuzzy theory, the segmentation performance of traditional spectral clustering algorithms is improved, and the comparative experiments also show that the algorithm greatly improves segmentation effect and computational complexity.