一种新的面向对象的概率图模型

针对大规模Bayes网络的知识表示和推理等问题，使用面向对象的方法扩展Bayes网络结构，提出了一种新的概率图模型——对象概率模型（OPM）．该模型充分利用层次结构中所蕴含的条件独立性，有效地降低了知识表示的复杂度．在Bayes网络消元推理算法的基础上设计了OPM的一种有效的推理算法，该算法可以根据需要调节推理的计算量，在一定程度上解决了概率推理的计算的复杂度问题．将OPM用于解决图像中文本的自动检测与定位问题，实验结果验证了模型的有效性．

A New Object Oriented Probabilistic Graphic Model

In this paper, a new object oriented probabilistic graphical model, named OPM, and its inference algorithm are proposed to solve the problems of knowledge expression and inference in large Bayesian networks. Firstly, the Bayesian network is segmented into several modules by the name of classes and a kind of object model is used to generate the OPM. OPM can make full use of the conditional independence in the hierarchical structure, which can reduce the complexity of the model construction and knowledge expression effectively. Secondly, an OPM based inference algorithm is proposed via the generalization of the elimination variable inference algorithm to realize the inference mechanism of the OPM. And the parameters in the algorithm can be adjusted according to specific problem to control the computation complexity of the inference process efficiently. And finally, the OPM is used in the automatic detection and location of texts in images to verify its validity. Experimental results show that OPM has not only a good result, but also a fast detection speed.