Saliency Detection via A Graph Based Diffusion Model
Saliency Detection via A Graph Based Diffusion Model
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DOI：
10.1007/978-3-319-58961-9_1
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发表时间：
2017-05
期刊：
影响因子：
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通讯作者：
Zhouqin He;Bo Jiang;Yun Xiao;C. Ding;B. Luo
中科院分区：
文献类型：
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作者：
Zhouqin He;Bo Jiang;Yun Xiao;C. Ding;B. Luo
本文提出了一种基于图的扩散方法，采用重启随机游走（RWR）模型来解决图像显着性检测问题。我们的方法首先分别计算输入图像的背景和前景先验。基于这些先验，我们进一步考虑图像的局部结构，采用RWR方法来获得更合理、更准确的背景和前景测量。最后，我们将背景和前景测量结合在一起以获得更准确的显着性估计。对四个基准数据集的实验评估证明了所提出方法的优点和有效性。
This paper proposes a graph based diffusion method for image saliency detection problem by adopting random walk with restart (RWR) model. Our method begins with computing background and foreground priors respectively for the input image. Based on these priors, we then adopt RWR method to obtain more reasonable and accurate background and foreground measurements by further considering the local structure of image. At last, we combine both background and foreground measurements together to obtain a more accurate saliency estimation. Experimental evaluations on four benchmark datasets demonstrate the benefits and effectiveness of the proposed method.