基于分水岭变换和蚁群聚类的图像分割

Image segmentation method based on watersheds and ant colony clustering

针对传统分水岭分割算法对噪声敏感和易于产生过分割问题,提出一种新的基于分水岭和蚁群智能聚类的图像分割方法(CWAC,Combining watersheds and ant colony clustering)。CWAC方法首先用分水岭变换对图像做初分割,然后用蚁群方法在区域之间进行聚类合并,获得最终的分割结果。CWAC不但成功地解决了分水岭存在的过分割问题,还大大提高了蚁群聚类算法的搜索效率;本文利用分水岭变换后的灰度信息和空间信息,定义了一种新的引导函数,可更准确有效引导蚁群聚类。实验结果表明CWAC可以快速准确地分割出目标,是一种有效的图像分割方法。

Aimed at resolving the problems of sensitivity to noise and over-segmentation existing in traditional watershed algorithm, a new image segmentation method -CWAC is presented. First, an image is separated into a large number of small partitions by watershed algorithm and the characteristic parameters are calculated. Second, CWAC method merges different regions of homogeneity with ant colony clustering algorithm to gain result of image segmentation. CAWC algorithm can successfully solve the over-segmentation problem and at the same time it can reduce the computational times of ant colony clustering. In order to be more accurate and efficient at clustering ant colony, a new visibility based on intensity distribution and spatial information is defined. Experimental results show that CWAC can segment objective quickly and accurately and it is a practicable method for the image segmentation .