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A Generic Probabilistic Graphical Model for Region-based Scene Interpretation

机译：基于区域的场景解释的通用概率图形模型

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The task of semantic scene interpretation is to label the regions of an image and their relations into meaningful classes. Such task is a key ingredient to many computer vision applications, including object recognition, 3D reconstruction and robotic perception. The images of man-made scenes exhibit strong contextual dependencies in the form of the spatial and hierarchical structures. Modeling these structures is central for such interpretation task. Graphical models provide a consistent framework for the statistical modeling. Bayesian networks and random fields are two popular types of the graphical models, which are frequently used for capturing such contextual information. Our key contribution is the development of a generic statistical graphical model for scene interpretation, which seamlessly integrates different types of the image features, and the spatial structural information and the hierarchical structural information defined over the multi-scale image segmentation. It unifies the ideas of existing approaches, e. g. conditional random field and Bayesian network, which has a clear statistical interpretation as the MAP estimate of a multi-class labeling problem. We demonstrate experimentally the application of the proposed graphical model on the task of multi-class classification of building facade image regions.
机译：语义场景解释的任务是将图像的区域标记为有意义的课程。此类任务是许多计算机视觉应用的关键因素，包括对象识别，3D重建和机器人感知。人造场景的图像表现出空间和分层结构的形式的强大上下文依赖性。建模这些结构是此类解释任务的核心。图形模型为统计建模提供了一致的框架。贝叶斯网络和随机字段是两个流行的图形模型，通常用于捕获这些上下文信息。我们的主要贡献是开发用于场景解释的通用统计图形模型，它无缝地集成了不同类型的图像特征，以及在多尺度图像分割上定义的空间结构信息和分层结构信息。它统一了现有方法的思想，即G。有条件的随机场和贝叶斯网络，它具有明确的统计解释作为多类标签问题的地图估计。我们通过实验证明了所提出的图形模型对建筑立面图像区域多级分类任务的应用。

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