中文摘要：

针对彩色图像分割算法中小目标区域容易错分割以及计算复杂度高的问题,提出一种基于HSI空间的结合粗糙集理论与分层思想的彩色图像分割方法。首先,由于彩色图像HSI空间的奇异点对应于RGB空间的灰色像素点,为了消除奇异点,在RGB空间寻找“灰色区域”进行分割与标记;然后,将图像转换到HSI颜色空间,在强度I分量上,考虑到空间邻域信息以及区域分布差异,设计了变阈值渐变性同质函数对原始直方图进行加权,将加权直方图和原始直方图分别作为粗糙集的上、下近似集,构造了新的粗糙度函数进行分割;其次,针对初分割得到的每个区域,在色调H分量上采用直方图阈值化完成细分割;最后,为了避免过分割,在RGB空间上进行区域合并。相比Mushrif等提出的粗糙集分割算法（MUSHRIF M M,RAY A K.Color image segmentation：rough-set theoretic approach.Pattern Recognition Letters,2008,29（4）：483-493）,该算法更容易分割出图像中的小目标区域,避免了因RGB三个分量的相关性造成的错误分割,算法运行速度提高了5~8倍。实验结果表明：该算法分割效果较好,具有一定的抗噪性与鲁棒性。

英文摘要：

Aiming at false segmentation of small regions and high computational complexity in traditional color image segmentation algorithm, a hierarchical method of color image segmentation based on rough set and HIS （ Hue-Saturation- Intensity） space was proposed. Firstly, for the reason that the singularities in HSI space are the achromatic pixels in RGB space, the achromatic regions of RGB space were segmented and labeled in order to remove the singularities from the original image. Secondly, the original image was converted from RGB space to HSI space. In intensity component, in view of spatial neighbor information and regional distribution difference, the original histogram was weighted by homogeneity function with changing thresholds and gradience. The weighted and original histograms were respectively used as the upper and lower approximation sets of rough set. The new roughness function was defined and applied to image segmentation. Then the different regions obtained in the previous stage were segmented according to the histogram in hue component. Finally, the homogeneous regions were merged in RGB space in order to avoid over-segmentation. Compared with the method based on rough set proposed by Mushrif etc. （MUSHRIF M M, RAY A K. Color image segmentation： rough-set theoretic approach. Pattern Recognition Letters, 2008, 29（4）： 483 -493）, the proposed method can segment small regions easily, avoid the false segmentation caused by the correlation between RGB color components, and the executing speed is 5 - 8 times faster. The experimental results show the proposed method yields better segmentation, and it is efficient and robust to noise.