Optimization of Segmentation Algorithms Through Mean-Shift Filtering Preprocessing
Optimization of Segmentation Algorithms Through Mean-Shift Filtering Preprocessing
复制标题
通过均值漂移滤波预处理优化分割算法
DOI：
10.1109/lgrs.2013.2272574
复制
发表时间：
2014-03-01
影响因子：
4.8
通讯作者：
Dai, Qinling
中科院分区：
文献类型：
--
作者：
Wang, Leiguang;Liu, Guoying;Dai, Qinling
这封信提出了一种改进的均值漂移滤波方法。该方法被添加为区域分割方法的预处理步骤，旨在以更通用的方式有利于分割。使用该方法，首先建立两个边缘线索和语义对象边界之间的逻辑回归模型。然后，模型预测边界后验概率，并将其与均值漂移滤波迭代中的权重相关联。最后，将滤波后的图像而不是原始图像放入分割方法中。在实验中，回归模型使用航拍图像进行训练，并使用航拍图像和 QuickBird 图像进行测试。采用两种流行的分割方法进行评估。定量和定性评估都表明，所提出的程序有助于获得优异的图像分割结果和更高的分类精度。
This letter proposes an improved mean-shift filtering method. The method is added as a preprocessing step for regional segmentation methods, which aims at benefiting segmentations in a more general way. Using this method, first, a logistic regression model between two edge cues and semantic object boundaries is established. Then, boundary posterior probabilities are predicted by the model and associated with weights in the mean-shift filtering iteration. Finally, the filtered image, instead of the original image, is put into segmentation methods. In experiments, the regression model is trained with an aerial image, which is tested with an aerial image and a QuickBird image. Two popular segmentation methods are adopted for evaluations. Both quantitative and qualitative evaluations reveal that the presented procedure facilitates a superior image segmentation result and higher classification accuracy.