一种特征加权FCM算法的图像重建技术研究

针对传统FCM(Fuzzy C-means)算法中初始聚类中心选取的随机性以及对初始值敏感的问题,提出一种基于进化策略的色彩空间加权的FCM聚类算法.通过在RGB(Red Green Blue)色彩空间矢量中设置加权矩阵来补偿各色彩的非均匀性,并采用一种类内最小距离最大的统计聚类算法来初始化聚类中心.实验结果表明,该算法能有效减少颜色量化后的均方差值,保持重建图像的整体层次和局部特征细节,对研究图像处理技术有较强的实际意义.

Abstract：

Aiming at a defect on randomness of the initial clustering center choosing and sensitivity of initial value in tradition FCM(fuzzy C-means) algorithm, a clustering algorithm about FCM of weighted color space based on evolutionary strategy is proposed. By interposing weighted matrix in RGB(Red Green Blue) color space, the color's inhomogeneous is compensated. And by using a statistics clustering algorithm of minimal maximal distance, clustering center is initiated. The experimental results show that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction, and has practical value to the study of the image process technology.

Research on image reconstruction technology by FCM of weighted characteristic

Aiming at a defect on randomness of the initial clustering center choosing and sensitivity of initial value in tradition FCM(fuzzy C-means) algorithm, a clustering algorithm about FCM of weighted color space based on evolutionary strategy is proposed. By interposing weighted matrix in RGB(Red Green Blue) color space, the color's inhomogeneous is compensated. And by using a statistics clustering algorithm of minimal maximal distance, clustering center is initiated. The experimental results show that the algorithm can decrease effectively the mean square deviation of color quantization, keep overall arrangement of ideas and part characteristic detail in image reconstruction, and has practical value to the study of the image process technology.