基于FCM的无监督纹理分割

由于图像所包含的纹理类别数目常常是未知的，因此无监督的纹理分类相比于有监督的纹理分类更具有实际的应用价值．从聚类的本质定义出发，采用了一种基于类内、类间距离比值的聚类有效性判别函数RII．为了减弱随着聚类数目的递增对判别函数带来的影响，分别采用最大类内距和最小类间距替代类内、类间距离之和作为判别因子．由于FCM的收敛速度与初始类别数目有一定的相关性，再引入收敛速度作为聚类有效性函数的惩罚因子，给出了一个新的判别函数nRII，有效地预防过分类现象，准确地评价了聚类结果．

Unsupervised Texture Segmentation Based on FCM

As the cluster number of texture in an image is always unknown, the unsupervised classification is more valuable than the supervised classification Based on the concept of a good cluster which should have the minimum intra-cluster distance and the maximum inter-clusters distance, the ratio of intra-cluster to inter-cluster distance is applied as the validity function However, the increase of initial cluster number will influence the sum of cluster diameters and the inter-cluster separation distance Therefore the maximum cluster diameter and minimum inter-cluster separation distance are provided instead, which is influenced by the initial cluster number more slightly and shows the essential of the cluster structure Due to the relationship of FCM convergent speed with the initial cluster number, the convergent speed is introduced as the penalization factor to the validity function and a new validity function nRII is proposed Compared with other validity functions, the nRII validity function can effectively prevent the over-clustering problem and give out a more exact estimation of the cluster number