We describe a general framework for probabilistic modeling of complex scenes and for inference from ambiguous observations. The approach is motivated by applications in image analysis and is based on the use of priors defined by stochastic grammars. We define a class of grammars that capture relationships between the objects in a scene and provide important contextual cues for statistical inference. The distribution over scenes defined by a probabilistic scene grammar can be represented by a graphical model, and this construction can be used for efficient inference with loopy belief propagation. We show experimental results with two applications. One application involves the reconstruction of binary contour maps. Another application involves detecting and localizing faces in images. In both applications, the same framework leads to robust inference algorithms that can effectively combine local information to reason about a scene.

中文翻译：

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场景语法、因子图和信念传播

我们描述了一个用于复杂场景的概率建模和从模棱两可的观察中推断的通用框架。该方法受到图像分析应用的启发，并基于使用由随机语法定义的先验。我们定义了一类语法，它们捕捉场景中对象之间的关系，并为统计推断提供重要的上下文线索。由概率场景语法定义的场景分布可以用图形模型表示，这种结构可以用于通过循环信念传播进行有效推理。我们展示了两个应用程序的实验结果。一种应用涉及二进制等高线图的重建。另一个应用涉及检测和定位图像中的人脸。在这两种应用中，