Random Walks on Graphs for Salient Object Detection in Images
Random Walks on Graphs for Salient Object Detection in Images
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DOI：
10.1109/tip.2010.2053940
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发表时间：
2010-12
影响因子：
10.6
通讯作者：
Viswanath Gopalakrishnan;Yiqun Hu;D. Rajan
中科院分区：
文献类型：
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作者：
Viswanath Gopalakrishnan;Yiqun Hu;D. Rajan
我们将图像中的显著对象检测问题表示为加权图的顶点上的自动标记问题。种子（标记）节点首先使用马尔可夫随机游走检测两个不同的图表示的图像上执行。虽然图像的全局属性是从完全图上的随机游走计算的，但局部属性是从稀疏k-正则图计算的。最显著的节点被选择为全局最孤立但福尔斯在局部紧凑对象上的节点。基于到最显著节点的基于随机游走的命中时间，进一步识别一些背景节点和显著节点。显著节点和背景节点将构成标记节点。一个新的图形表示的图像，更准确地表示节点之间的显着性，“弹出图”模型，进一步计算的基础上的知识标记的显着和背景节点。一个半监督学习技术是用来确定标签的未标记的节点，通过优化一个平滑的目标标签函数上的新创建的“弹出图”模型沿着与一些加权软约束的标记的节点。
We formulate the problem of salient object detection in images as an automatic labeling problem on the vertices of a weighted graph. The seed (labeled) nodes are first detected using Markov random walks performed on two different graphs that represent the image. While the global properties of the image are computed from the random walk on a complete graph, the local properties are computed from a sparse k-regular graph. The most salient node is selected as the one which is globally most isolated but falls on a locally compact object. A few background nodes and salient nodes are further identified based upon the random walk based hitting time to the most salient node. The salient nodes and the background nodes will constitute the labeled nodes. A new graph representation of the image that represents the saliency between nodes more accurately, the “pop-out graph” model, is computed further based upon the knowledge of the labeled salient and background nodes. A semisupervised learning technique is used to determine the labels of the unlabeled nodes by optimizing a smoothness objective label function on the newly created “pop-out graph” model along with some weighted soft constraints on the labeled nodes.