Parts based generative models for graphs
Parts based generative models for graphs
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基于零件的图形生成模型
DOI：
10.1109/icpr.2008.4761206
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Richard C. Wilson
中科院分区：
文献类型：
--
作者：
D. H. White;Richard C. Wilson
生成模型在统计模式识别领域是众所周知的。通常，它们描述向量空间中模式的概率分布。相比之下，很少的工作已经做了生成模型的图，因为图没有一个直接的矢量表示。在本文中，我们研究的问题，创建生成分布集的图。我们通过观察每个图中存在哪些子图以及这些子图是如何连接的来模拟一组图中的变化。通过对子图进行聚类，我们可以将具有相似结构的子图分组。然后在每个图中存在的聚类上定义分布，每个聚类中存在哪些子图以及子图的连接方式。然后可以通过从分布中采样来生成新的图。我们展示了我们的方法的实用性，综合生成的点集和点集来自现实世界的图像连接的对象。
Generative models are well known in the domain of statistical pattern recognition. Typically, they describe the probability distribution of patterns in a vector space. In contrast, very little work has been done with generative models of graphs because graphs do not have a straight-forward vectorial representation. In this paper we examine the problem of creating generative distributions over sets of graphs. We model the variation in a set of graphs by observing which subgraphs are present in each graph and how these subgraphs are connected. By performing clustering on the subgraphs we can group those with similar structure. Distributions are then defined on the clusters present in each graph, which subgraphs are present in each cluster and the way subgraphs are connected. New graphs can then be generated by sampling from the distributions. We show the utility of our approach on synthetically generated point sets and point sets derived from real-world imagery of articulated objects.