基于图模型的图像分析-计算机应用技术专业论文.docx

基于图模型的图像分析研究 摘要 目标识别、场景分析是图像理解的重要内容。如何构建一种通用有效的反 馈的框架，适用于多类别目标场景图像分析，是计算机视觉领域的研究难点。 本文以图像中有何种目标、目标间的关系、图像是什么场景为研究方向，主要 包括两方面：一是场景中的目标识别，二是场景图像分析。目标识别具有主动 性，是为了对场景进行更好的解释，是场景分析的基础；场景分析为目标识别 提供先验信息，可以指导目标识别；二者相互关联、约束。本文主要工作如下： 1) 分析了图结构模型的类别及其各自特性，介绍了有向图和概率论如何运 用于贝叶斯框架下的生成模型中，并描述了图模型下的三种 Dirichlet 分布及其 概率采样方法。 2) 实现了层次 Dirichlet 过程下的场景图像单目标识别和转换 Dirichlet 过程 下的场景图像多目标识别。前者根据分层思想将场景图像看做“目标 -部分 -区 域”，分别计算上层标记所对应的下层数据的概率分布，根据概率确定标记。在 此基础上后者进一步融入目标的空间位置转换 信息，根据目标类别位置信息的 概率分布情况，可在场景图像的不同位置生成 多个目标实例，从而实现多目标 类别及实例的识别。 3) 研究了有向图模型下分层次的、融入上下文知识的场景分析。采用场景 级的全局上下文信息为目标识别提供先验信息 ，采用目标级的局部上下文信息 对多目标识别加以约束，目标识别结果可以反 馈作用于场景分析，形成一个反 馈的过程。 关键字：有向图模型，Dirichlet 过程，目标识别，上下文信息，场景分析 Research of Image Analysis based on Graph Model Abstract The core contents of image understanding are object recognition and scene Analysis. In computer vision field, one of the most difficult problems is how to build a common feedback framework which can effectively recognize objects and analysis kinds of scene image. This thesis is aim at recognizing what kinds of objects are appearing in the image, finding the relationship between objects and analyzing the scene. The main task contains two aspects, one is objects recognition in the image, the other is scene analysis of the image. Objects recognition as the basis of scene analysis is benefit for explain the detail of the scene. Conversely, scene analysis which providing the prior knowledge can guide objects recognition. Therefore these two tasks are strongly connecting with each other. This thesis is mainly about as follows: Analysis two kinds of graph model and their characters. Then introduction how to use direct graph and probability in generative model. Meanwhile, three kinds of Dirichlet process sampling used in graph model are described. Achieved the single object recognition based on Hierarchical Dirichlet Process and multi-objects recognition based on Transformed Dirichlet Process. The former considers images as “object – part – region”, then compu