一种新的面向对象的概率图模型  

A New Object Oriented Probabilistic Graphic Model

作　　者：汪荣贵[1] 高隽[1] 张佑生[1] 彭青松[1] 

机构地区：[1]合肥工业大学计算机与信息学院,合肥230009

出　　处：《计算机研究与发展》2005年第8期1283-1292,共10页Journal of Computer Research and Development

基　　金：国家自然科学基金项目(60175011;60375011);安徽省自然科学基金项目(03042207);安徽省优秀青年科技基金项目(04042044)

摘　　要：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.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.

关 键 词：BAYES网络 Bayes网络库 消元算法 对象模型 对象概率模型 概率图模型 面向对象 概率模型 图模型 推理算法 

分 类 号：TP311.52[自动化与计算机技术—计算机软件与理论] TP391.41[自动化与计算机技术—计算机科学与技术]