摘要
提出一种融合纹理特征的两阶段聚类分割算法。首先,选择纹理特征、差分均值和颜色分量这3个特征,组成一个分割所用的特征矢量;然后,使用直方图对特征矢量进行初始聚类中心和类别数的估算;最后,通过模糊C均值算法对特征矢量进行聚类。该算法有效地克服了模糊C均值(FCM)容易陷入局部最优的缺陷,使聚类结果更加精确。实验结果表明该方法比一些现存方法的分割效果要好。
It proposes texture feature fusion-based two-stage clustering segmentation algorithm. First, we choose texture feature, the average of difference and color component as feature vector for segmentation. Then, at the stage of segmenta- tion, aim to the disadvantages of Fuzzy c-means, it computes the clustering center and the number of clustering center based on histogram. Finally, we use feature vector to cluster through Fuzzy c-means. Compared with some well-known methods, the proposed method has a better segmental result.
出处
《中国图象图形学报》
CSCD
北大核心
2012年第9期1075-1084,共10页
Journal of Image and Graphics
关键词
局部二进制模式
模糊C均值
聚类分割
直方图
local binary pattern
fuzzy c-means
clustering segmentation ~ histogram
