基于粒子群的改进模糊聚类图像分割算法

Improved fuzzy clustering image segmentation algorithm based on particle swarm optimization

基于粒子群优化的改进模糊聚类图像分割算法将微粒群搜索聚类中心作为图像分割的聚类初值,克服了FCM分割算法对聚类中心初值敏感的缺点,大幅提高了图像分割算法的计算速度。改进的模糊聚类图像分割算法,一方面考虑到像素的空间位置信息和相互邻域之间像素有很大的相关性,在目标函数中引入邻域惩罚函数;另一方面提出聚类在二维方向上进行更新的思想,建立了包含邻域单元熵的新聚类目标函数。实验结果表明,该方法可以使模糊聚类的速度得到明显提高,对初始聚类中心不敏感,抗噪能力强,是一种有效的模糊聚类图像分割方法。

In improved fuzzy clustering image segmentation method based on Particle Swarm Optimization（PSO_TDFCM）, the clustering centers searched by particle swarm are taken as image segmentation clustering initializations, which overcomes the sensitive to the clustering center initializations for Fuzzy C-Means （FCM） algorithm as well as improves the speed of FCM algorithm greatly. Meanwhile, on the one hand, the new idea taken into account the great correlation between the spatial site information of a pixel and it＇ s neighboring pixels, consequently, the neighboring penalized function is added in the objective function; on the other hand, it suggests to update the clustering centers at the two-dimension directions, from which the new objective function combines cell entropy. The results of comparative experiments demonstrate that this approach is an effective fuzzy clustering image segmentation algorithm, which can make a marked improvement in the speed of fuzzy clustering as well as insensitive to the initial clustering patters and robust to the noise.