基于区域特征分析的快速FCM图像分割改进算法

提出一种基于图像区域特征估计聚类数的快速FCM图像分割算法。在算法的预测分析阶段， 利用由共生矩阵统计值所构成的特征矢量描述图像中区域特征并结合多个聚类有效性判定函数实现准确的聚类数估计和隶属度矩阵值的初始化。在主聚类阶段，采用Gabor滤波器提取的颜色纹理隐式混合特征进行聚类，不但能获得更加合理的区域分割质量，同时也具有较好的抗噪声能力。实验表明改进算法有效克服基于像素点级特征的FCM图像分割算法在聚类数估计和隶属度矩阵初始化方面的不足，加快FCM主聚类阶段的迭代速度，执行效率更高。

An Improved Fast FCM Image Segmentation Algorithm Based on Region Feature Analysis

A fast image segmentation algorithm based on region feature is proposed to estimate centroid number. In the preprocessing analysis stage, the feature vector based on the cooccurrence matrix statistics is used to describe the regional characteristics of sub-image, and the proposed algorithm combines with cluster validity function to estimate accurate centroid number and initialization of membership matrix. In the main clustering stage, the implicit feature of color and texture extracted by Gabor filter is used to accomplish clustering, which not only produces a more reasonable quality of region segmentation, but also has fine noise immunity. The experimental results show that the proposed algorithm effectively overcomes the deficiencies of pixel-level estimations, greatly accelerates the iterative speed of the FCM main clustering stage and achieves higher efficiency in the implementation.