(BP)2: Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies
(BP)2: Beyond pairwise Belief Propagation labeling by approximating Kikuchi free energies
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DOI：
10.1109/cvpr.2008.4587371
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发表时间：
2008-06
期刊：
影响因子：
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通讯作者：
Ifeoma Nwogu;Jason J. Corso
中科院分区：
文献类型：
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作者：
Ifeoma Nwogu;Jason J. Corso
信念传播（BP）对于图的近似推理是非常有用和有效的。但是，当图与强烈的冲突相互作用高度相连时，BP往往无法收敛。广义信念传播（GBP）通过近似菊池自由能，在这种图上提供了更精确的解，但是菊池近似所需的聚类很难生成。我们提出了一种新的算法，在三角剖分过程中，在不成倍增加图的大小的情况下，从图中生成这样的聚类。为了执行统计区域标记，我们为图的节点引入了超像素的使用，因为它比像素网格更自然地表示图像。这导致了一个更小但更紧密相连的图，其中BP始终失败。我们演示了我们版本的GBP算法如何在合成图像和自然图像上优于BP，并且在这两种情况下，GBP仅在几次迭代后收敛。
Belief propagation (BP) can be very useful and efficient for performing approximate inference on graphs. But when the graph is very highly connected with strong conflicting interactions, BP tends to fail to converge. Generalized Belief Propagation (GBP) provides more accurate solutions on such graphs, by approximating Kikuchi free energies, but the clusters required for the Kikuchi approximations are hard to generate. We propose a new algorithmic way of generating such clusters from a graph without exponentially increasing the size of the graph during triangulation. In order to perform the statistical region labeling, we introduce the use of superpixels for the nodes of the graph, as it is a more natural representation of an image than the pixel grid. This results in a smaller but much more highly interconnected graph where BP consistently fails. We demonstrate how our version of the GBP algorithm outperforms BP on synthetic and natural images and in both cases, GBP converges after only a few iterations.