【作者】 徐丽；

【导师】 巨永锋；

【作者基本信息】 长安大学 ， 交通信息工程及控制， 2013， 博士

【摘要】 高精度的图像标记这一技术难题制约了图像检索的应用水平，而表达与利用图像空间上下文及语义信息是有效解决这一难题的关键技术之一。条件随机场（CRFs）是标记和分割结构化数据的概率框架，它在表达大尺度的图像空间上下文和语义特征以及后验概率建模这两方面有其独特的优势，基于这一概率框架可以有效地降低图像标记中的不确定性，提高图像标记的精度。基于CRFs理论的上述优势，本文针对面向标记的目标类图像分割中仍存在的问题，开展了考虑目标多尺度特性及语义特征的二阶/高阶CRFs模型的研究。研究的主要内容及创新点如下：针对目标类图像分割中，如何更好地表达目标空间上下文及语义信息这一问题，本文提出了借助于支持向量机（SVM）将概率潜在语义分析（PLSA）技术与CRFs模型框架有机集成的方法，以实现多类标记下高精度的图像分割。具体地说，将PLSA作为一种特征变换技术衍生出的目标语义特征来作为SVM分类器的输入，并利用此SVM的概率输出来对CRFs模型的关联势函数进行定义，用相邻超像素的特征对比度函数及Potts模型对交互势函数进行了定义，从而建立了一个基于PLSA技术的二阶CRFs模型（简称为PLSA-CRFs）。其中，在PLSA模型建立过程中引进了一种高效的期望最大化（EM）算法来实现PLSA监督参数估计，以改善标准EM算法参数估计效率低的问题。最后，开展了PLSA-CRFs模型的分段参数训练，以及基于环状置信传播（LBP）的模型近似推理方法研究。针对“单一尺度的CRFs模型不能描述图像目标多层次的空间结构及语义关系”的问题，提出了一种融合目标多级空间上下文及语义关系的二阶RF-CRFs模型以实现高精度的目标类图像分割。具体内容包括：开展了目标分层特征提取及特征提升方法的研究；提出了融合目标三个层级空间上下文及目标语义关系的二阶RF-CRFs模型，其中，该模型是以随机森林（RF）分类器来定义其关联势函数，以多级特征对比度加权的Potts函数来定义交互势函数。最后，开展了该RF-CRFs模型的极大伪似然参数训练，以及基于环状置信传播算法的模型推理方法研究。针对“基于像素的二阶CRFs模型图像标记效率低”、“基于超像素的二阶CRFs模型中分割尺度及图像过分割质量对标记结果影响较大”以及“二阶CRFs模型空间交互能力有限”的问题，开展了基于标记一致性软约束和考虑图像过分割质量的高阶CRFs模型的创新性研究。具体内容包括：开展了上述模型在多目标类分割问题中的实验研究，并在高阶势函数的定义中，提出了一种面向标记的分割质量敏感性函数的定义方法。最后，对上述高阶CRFs模型开展了启发式分段学习的参数训练方法，以及基于变换的α-膨胀推理方法的研究。在实验中，以自然图像为测试数据，开展了上述三类模型及其相关标记方法的测试及性能评价，证明了上述方法在复杂场景的多目标图像标记中的有效性。更多还原

【Abstract】 It is a difficulty of image labelling ensuring high precision that prevents the rapidextension to so many applications such as image retrieval, the expression and modelling ofspatial context and semantic information from a given image is a major key technology tosolve the difficulty in image labelling. Conditional Random Fields（CRFs）is a probabiliticsframework for labeling and segmenting structural data. In the application of Image processing,CRFs have the unique advantages over large scale spatial dependency, semantic informationand directly modelling posteri probability of a given image. Thus, the framework is capable toreduce uncertainty and improve the precision in image labelling.Owe to the advantages mentioned above of CRFs theory, for some problems existing inobject class image segmentation based on image labelling, the thesis develops the studies onlow and higher order CRFs model when considering multi-scale intrinsic properties andsemantic feature of image, the details and innovative studies are in the following:For the purpose of better express spatial, even semantic information of image, it isproposed that Probability Latent Semantic Analysis（PLSA）technology has been integratedinto the framework of CRFs via Support Vector Machine(SVM) classifier in order to achievean improved precision of object class segmentation in multi-label case. Details are as follows:Firstly, semantic features were derived from a PLSA clustering procedure can be an input toSVM classifier. Then, an output in the form of probability value from the well-defined SVMclassifier was used to make the associative potential function, Potts function and featurecontrast function were used to define the interactive potential function, then a PLSA-CRFscan be formed. In the process, an efficient Expectation Maximization（EM） algorithm wasintroduced to better estimate the parameters of PLSA model in order to improve less efficientproblem using standard EM algorithm. Finally, piecewise training algorithm is used toestimate the parameters of the proposed model, and Loopy Belief Propagation （LBP）algorithm is used to approximately infer the model.For the problem “superpixel-based CRFs using features only from a scale can’t expressinherent multi-level spatial structure and semantic relation of an object”, the thesis develops apairwise superpixel-based CRFs model with association term defined as an output of RandomForest classifier based on multi-level spatial context and semantic features of objects so as toacquire a high precision in object class image segmentation. Main studies include: multi-levelspatial context feature extraction and boosted feature selection method, the definition ofassociative potential using Random Forest, and interaction potential weighted by common boundary of neighbors for CRFs model. Finally, pseudo-maximum likelihood method wasused to train the model for parameters estimation, and LBP algorithm was used to infer thepiecewise model.For the problem of “the pixel-based CRFs is low efficient”,“the scale and quality ofimage over-segmentation have greater effects on superpixel-based CRFs, which have limitedability of spatial interaction” for image labeling, the thesis carried out the studies and makesimprovements on higher order CRFs model based on label consistency soft constraint andquality of image over-segmentation proposed by Kohli. Details are as follows: the model waswell studied by experiments on complex scenes at the first. A new segmentation qualitysensitive function for the purpose of image labelling (not image over-segmentation) was thenproposed, thus higher order potential was updated. Finally, the improved HoCRF in the thesisis trained by piecewise learning algorithm and inferred by transformed α-expansion based ongraph cut method.Experiments on images including natural, aerial imagery with complex scene are carriedto verify the three proposed models and some relative methods. The results have shown thosemethods are effective to object class segmentation in multi-label case for those images such ascomplex natural scene and aerial images.更多还原