基于显著性密度修正的均值漂移分割算法

针对固定空间和色彩带宽的均值漂移分割算法无法解决的错分割问题,提出一种基于显著性特征进行密度修正的均值漂移分割算法。首先基于密度估计的主颜色量化结果计算区域视觉显著性;其次,将区域视觉显著性融合像素级显著性作为色彩特征空间聚类的密度修正因子,将密度修正后的融合图像作为输入执行均值漂移分割;最后进行小区域合并获得最终分割结果。实验结果显示,所提分割算法在四种尺度上的真实边界准确率和召回率平均值达到0.64和0.78,与其他方法相比,分割精度有显著的提高;同时,在视觉上有效提高了目标完整性,增强了自然图像中目标分割的鲁棒性。

Mean-shift segmentation algorithm based on density revise of saliency

To solve the fault segmentation of the mean shift segmentation algorithm based on the fixed space and color bandwidth, a mean-shift segmentation algorithm based on the density revise with saliency feature was proposed. A region saliency computing method was firstly proposed on the basis of density estimation of main color quantization. Secondly, region saliency was fused with pixel level saliency as density modifying factor, and the fused image was modified as input for mean-shift segmentation. Finally, the scatter regions were merged to obtain the final segmentation results. The experimental results show that for the truth boundaries, the average precision and recall of the proposed segmentation algorithm are 0.64 and 0.78 in 4 scales. Compared with other methods, the accuracy of the proposed segmentation method is significantly improved. It can effectively improve the integrity of the target and the robustness of natural color image segmentation.