彩色图像的球形粒计算分割算法

Color Image Segmentation Algorithm Based on Spherical Granular Computing

针对基于聚类的彩色图像分割算法速度较慢,提出了彩色图像的球形粒计算分割算法.将彩色图像每个像素点表示为以该点RGB像素值为中心0为半径的球形粒,设计球形粒之间的合并算子,利用粒度阈值对两球形粒进行有条件合并,得到不同粒度的球形粒组成的球形粒集,以球形粒中心对应的RGB值代替球形粒包含像素点的RGB值.实验结果表明:与K-means算法和FCM算法相比,球形粒计算分割算法是稳定的而且分别加快了6倍和34倍.

Considering the low segmentation speed of color image segmentation based on clustering method, a color image segmentation algorithm was proposed based on spherical granular computing. For a color image, each pixel point was represented as a sphere with the center RGB and radii 0, the union operator between two spherical granules was designed, the granularity threshold was used to unite two sphere granules and obtain the sphere granules with different granularities. The RGB of pixels belonging to the sphere granule was replaced by that of the sphere granule＇ s center. The experimental results showed that the spherical granular computing segmentation algorithm was stable and speeded up 6 times and 34 times compared with K-means and FCM segmentation algorithms, respectively.