Abstract
We describe a general framework for probabilistic modeling of complex scenes and 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 different 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.
Abstract (translated)
我们描述了复杂场景的概率建模和模糊观察的推理的一般框架。该方法的动机是图像分析中的应用，并且基于随机语法定义的先验的使用。我们定义了一类语法，它捕获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上的分布可以由图形模型表示，并且该构造可以用于利用循环信念传播的有效推断。 我们用两种不同的应用展示实验结果。一个应用涉及二进制等值线图的重建。另一个应用涉及检测和定位图像中的面部。在两个应用程序中，相同的框架导致强大的推理算法，其可以有效地将本地信息与场景的推理相结合。
URL
https://arxiv.org/abs/1606.01307
