摘要
为提高算法的普适能力,提出了一种新的基于特征散度的模糊彩色图像分割算法(FD-CIS).算法引入了特征散度和模糊相异性函数来度量差异性,利用特征散度进行数据聚类,实现图像的区域融合.实验证明,算法较好地降低了彩色图像大样本数据的运算量,简单而有效地解决了过度分割现象,避免了聚类算法对初始条件的依赖性,与人的主观视觉感知具有良好的一致性.
In order to improve general adaptive capability of algorithm, the new color image segmentation algorithm based on feature divergence and fuzzy theory （FDCIS） is proposed. The algorithm introduces feature divergence and fuzzy dissimilarity function into calculation in order to measure the dissimilarity of feature vector, clusters data by means of feature divergence, and accomplishes the merge of image region. The experimental results demonstrate that the color image segmentation result of the proposed approach reduce calculation on large sample of color image, simply and effectively solve over-segmentation of color image, avoid the dependence of the algorithm on initial condition, and hold favorable consistency in terms of human perception.
出处
《重庆大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第1期89-92,共4页
Journal of Chongqing University
基金
重庆市自然科学基金资助项目(CSTC2005BA2002)
关键词
特征散度
模糊相异性
聚类
彩色图像分割
feature divergence
fuzzy dissimilarity
clustering
color image segmentation
