摘要
以少数民族服饰图像为分割对象,结合块截断算法设计思想,提出一种基于空间邻域的模糊C均值图像分割算法。利用方块截断编码理论将图像RGB颜色空间分量截断为6个分量,通过六维特征向量对民族服饰图像进行特征表示,将其作为算法输入进行聚类分割。实验结果表明,该算法在分割精度、划分系数和划分熵3个量化指标上的性能均优于FCM,FCM_S1和FCM_S2算法,对民族服饰图像的分割效果较好,尤其表现在对民族服饰具有代表性的特征元素区域分割上。
By taking the image of ethnic minority costumes as the segmentation object, based on block truncation theory,an algorithm for image segmentation based on spatial neighborhood of Fuzzy C-means （FCM） algorithm is proposed. Firstly,the Block Truncation Coding（BTC） theory is used to cut the image RGB color space into six components. Then, the six-dimensional feature vector is used to express the characteristics of the national costume image. Finally, this six-dimensional feature vector is used as the data input of the algorithm. Experimental results show that the performance of the proposed algorithm in this paper is better than FCM, FCM $1, FCM_S2 in segmentation accuracy, partition coefficient and partition entropy. It has better performance on ethnic costume image segmentation, especially for typical elements of ethnic constume.
出处
《计算机工程》
CAS
CSCD
北大核心
2017年第5期261-267,274,共8页
Computer Engineering
基金
国家自然科学基金(61462097
61262071)
国家科技支撑计划项目(2013BAJ07B00)
云南省科技厅应用基础研究计划项目(2014FD016)
关键词
民族服饰
块截断编码
图像分割
空间邻域
模糊C均值算法
ethnic costume
Block Truncation Coding （BTC）
image segmentation
spatial neighborhood
Fuzzy C-means （ FCM ） algorithm
