基于可分解马尔科夫网的视频图像检测方法研究

研究了可分解马尔科夫网的概念、方法，分析了可分解马尔科夫网在序列图像数据挖掘中的作用与意义，并直接将马尔科夫网的结构作为决策或推理依据，应用于问题求解，拓广了可分解马尔科夫网应用的可能性；以真实交通违章的视频图像为例，以多种粒度(节点数)广泛研究建立视频图像间的可分解马尔科夫网并分析其对问题的适用性，通过网络结构分析来检测视频图像间的差异，从而发现某种有意义的模式(例如，交通违章)；仿真结果表明所提方法具有实用价值和较好效果；研究结果表明可分解马尔科夫网可以很好地揭示数据间的抽象近邻关系，并且这种网络具有很好的知识表达和逻辑推理的作用，是重要的模式识别方法。

Study on Approach of Detection for Video Image Based on Decomposable Markov Network

The concept and method of decomposable Markov network (DMN) are sdudied, and the role of DMN in data mining from sequence images is analyzed. The construct of DMN is straight utilized as the evidence of inference or decision for problem solution and enlargement of application possibility in DMN. For example of the factual video images with traffic rule violation, we deeply investigate several graininess (nodes) which are used in constructing DMN between video images, assess its applicability for our problem, and detect abnormality in them by analyzing construct of DMN for finding a interesting mode. It is showed that the method is feasible and effective according to the result of simulation. The researches indicate that the DMN may apropos reveal abstract adjacent relations existing in data, has capabilities of showing knowledge and logic reasoning, and also is important method of pattern recognition.