Approximate Belief Propagation by Hierarchical Averaging of Outgoing Messages
Approximate Belief Propagation by Hierarchical Averaging of Outgoing Messages
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DOI：
10.1109/icpr.2010.338
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
K. Ogawara
中科院分区：
文献类型：
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作者：
K. Ogawara
本文提出一种近似置信传播算法，该算法用平均传出消息替换节点的传出消息，并将消息从低分辨率图分层传播到原始图。当应用于图像时，与标准置信传播算法相比，所提方法将计算时间减少了一半或三分之二，并将所需内存量减少了60%。所提方法在CPU和GPU上实现，并与标准置信传播算法进行比较，依据Middlebury立体基准数据集进行评估。结果表明，所提方法在计算时间和所需内存量方面均优于其他方法，且精度损失较小。
This paper presents an approximate belief propagation algorithm that replaces outgoing messages from a node with the averaged outgoing message and propagates messages from a low resolution graph to the original graph hierarchically. The proposed method reduces the computational time by half or two-thirds and reduces the required amount of memory by 60% compared with the standard belief propagation algorithm when applied to an image. The proposed method was implemented on CPU and GPU, and was evaluated against Middlebury stereo benchmark dataset in comparison with the standard belief propagation algorithm. It is shown that the proposed method outperforms the other in terms of both the computational time and the required amount of memory with minor loss of accuracy.