一种基于自适应标记与区域间近邻传播聚类的分水岭图像分割算法

A Watershed Image Segmentation Algorithm Based on Self-adaptive Marking and Interregional Affinity Propagation Clustering

分水岭算法是一种高效的图像分割算法,能够准确地对图像进行基于区域的分割,但是存在易过分割的问题.为此本文提出一种改进的分水岭算法:首先,对彩色图像进行频谱包络滤波并计算彩色梯度获得梯度图像,再采取一种自适应设定参数的H-minima技术,对梯度图像的极小值区域进行标记;然后,对已标记极小值区域的梯度图像进行分水岭分割;最后,计算分水岭分割所得各区域的颜色矩,作为该区域的颜色特征,并对这些区域进行近邻传播聚类获得分割结果.通过与近年来其它改进的分水岭算法和采用聚类的图像分割算法实验比较,本文所提算法能更加有效地抑制过分割,提高分割准确率,具有良好的自适应性和鲁棒性.

The watershed algorithm can conduct region-based image segmentation effectively and accurately,but it tends to cause over-segmentation. To tackle the above mentioned problem,an improved watershed algorithm is proposed,as follows：first of all,the color gradient is computed using spectrum envelope filtered color image,based on which,regions with minimum gradient are marked using self-adaptive H-minima transformation method. Then,the watershed transform is applied to segment the marked gradient image. Finally,affinity propagation clustering is adopted to merge the regions segmented by the watershed transform,using color moments computed on each local region,to get the final segmentation result. Experiments conducted on public available datasets demonstrate the adaptability and robustness of proposed algorithm,compared with the relative state-ofthe-art methods. The proposed method can solve the over-segmentation problem well and get accurate results.