摘要
针对目标在图像边界上带来的检测误差,提出了边界显著性算法。首先在多尺度下对图像进行超像素分割,计算边界差异,估计其边界显著性。而后对所有超像素进行模式挖掘,得到显著性种子,并与边界显著性相结合。最后通过显著性传播得到最终显著图。在三个公开的测试数据集上将本文提出算法与其他18种主流的现有算法进行对比。大量实验结果表明,所提出的算法在不同数据集上都优于目前主流算法。
In view of the detection error caused by the target on the image boundary,this paper proposes a boundary saliency algorithm. We firstly conduct a multi-scale image segmentation at super-pixel level and compute the boundary discriminations to estimate its boundary saliency. Then reliable saliency seeds are obtained by pattern mining with the boundary saliency. Finally,the saliency maps are obtained by saliency propagation. We compare our model to other 18 saliency detection algorithms and extensive experiments on three datasets. The results show that the proposed model outperforms state-of-the-art methods.
出处
《微型机与应用》
2017年第8期34-38,共5页
Microcomputer & Its Applications
基金
国家自然科学基金(61501509)
