由于分水岭方法进行图像分割时经常是在梯度图像上进行,并经常产生过分割的结果,因此为克服图像过分割问题和提高分割的准确性,提出了一种基于分水岭和重叠率衡量分层融合策略的彩色图像分割新算法——HWO。该算法首先将RGB颜色空间转化到Lab颜色空间,并根据a、b维来提取统计2维直方图,同时在直方图上运用分水岭分割方法,通过对峰进行填充来得到图像的初步分割结果;然后将与填充对应的分割区域样本与高斯分布结合起来,对图像进行高斯混合模型假设下的参数估计;最后对模型与模型间进行重叠率衡量及分层区域融合,以得到最终的图像分割结果。实验中,首先采用训练图像集对算法涉及的两个参数进行确定,然后对测试图像集的分割效果和分割时间性能进行评估,评估是以标准的人工分割图像库为基准的。实验结果表明,该算法可解决过分割问题,其评估所得分准率及分全率综合衡量系数为0.609,而人工分割综合衡量系数为0.79,同时新方法的分割时间仅为传统方法的1/3,分割速度有了较大提高。Watershed segmentation based on gradient images usually has over-segmentation result.To solve over-segmentation problem,we propose a new Hierarchical image segmentation method based on Watershed filling and Overlap-rate measuring(HWO).Firstly,we transform RGB color space to Lab and statistic the histogram according to a and b dimensions.The watershed segmentation algorithm is applied to 2D histogram and the initial segmentation result is achieved.Then,we associate the segmentation region with the Gaussian distributing,and estimate the parameter value.Finally,we measure the Overlap-rate for a hierarchical region merging and get the final result.In the experiment,the two parameters are determined.We then evaluate the segmentation performance with a standard database of human segmented natural images.Results show our method can efficiently solve over-segmentation problem,and the combined value of precision and recall measures is 0.609,while is 0.79 when the segmentation is done manually.In addition,the new method also has much less computing complexity.教育部“211”计划“985”工程-2期项目(000-X07204);; 国家高技术研究发展计划(863)项目(2006AA01Z129
