基于伪并行免疫遗传算法和粗糙集的图像分割方法

Image segmentation method based on pseudo-parallel immune genetic algorithm and rough set

提出了一种结合伪并行免疫遗传算法和粗糙集的新的图像分割方法,该方法适用范围广。以各抗体子群体适应度为依据构建属性值表,基于属性值的不可分辨关系将图像分成许多小区域;通过值约简、属性权值、区域差异度计算得到各基本区域之间的差异度矩阵,进而对基本区域进行相似域和非相似域的划分,最后基于相似度的最终等价关系及分割来实现基本区域的合并完成图像分割。实验结果验证了该方法的有效性。

One new image segmentation method based on pseudo-parallel immune genetic algorithm and rough set is proposed. This method suitable scope is larger. Frist, the attributes grid is based on every antibody of sub-population fitness. The relations which cannot distinguishing is based on the attribute value to divide the image into many small regions. Then, the dissimilaritie matrix of every basic area is calculated by value reduction, the weights of the attributes and the dissimilarities of the regions in order to divide basic area into the similarities and dissimilarity the regions. Lastly, the final equivalent relations based on the similarities are used to implement the combination of the similar regions and the image segmentation is accomplished. The experimental result has confh＇med this method validity.