Salient object detection via nonlocal diffusion tensor
Salient object detection via nonlocal diffusion tensor
复制标题
通过非局部扩散张量检测显着目标
DOI：
10.1142/s0218001415550137
复制
发表时间：
2015
影响因子：
1.5
通讯作者：
Baciu George
中科院分区：
文献类型：
--
作者：
Zhang Xiujun;Xu Chen;Sun Xiaoli;Baciu George
在本文中，视觉注意力扩散被表述为非局部扩散方程。与其他基于扩散的方法不同，引入非局部扩散张量来同时考虑扩散强度和扩散方向。借助扩散张量，沿着主方向，抑制扩散以保留前景和背景之间的差异，而在其他方向上，则增强扩散以组合相似区域并整体突出显着对象。通过两阶段扩散，得到最终的显着图。对三个广泛使用的基准数据集（即 MSRA-ASD、MSRA-B 和 PASCAL-1500 数据集）进行了广泛的定量或视觉比较。实验结果证明了我们的方法的优越性能。
In this paper, visual attention spreading is formulated as a nonlocal diffusion equation. Different from other diffusion-based methods, a nonlocal diffusion tensor is introduced to consider both the diffusion strength and the diffusion direction. With the help of diffusion tensor, along with the principle direction, the diffusion has been suppressed to preserve the dissimilarity between the foreground and background, while in other directions, the diffusion has been boosted to combine the similar regions and highlight the salient object as a whole. Through a two-stages diffusion, the final saliency maps are obtained. Extensive quantitative or visual comparisons are performed on three widely used benchmark datasets, i.e. MSRA-ASD, MSRA-B and PASCAL-1500 datasets. Experimental results demonstrate the superior performance of our method.