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中文摘要：
图结构的特征提取及相似性度量是计算机视觉和模式识别中的重要研究内容。针对传统的方法对存在非刚性变换的图结构难以充分描述这一问题，给出一种基于图的上下文（GC）描述子的图结构信息描述及距离度量方法。首先，通过对图的边缘进行等距离散取样得到该图的采样点集；其次，基于图的采样点集给出图的上下文描述子；最后，采用推广的推土机距离（EMD）方法实现图的上下文描述子的距离度量。不同于图的编辑距离计算方法，所提方法不需要定义代价函数。实验表明该方法对于一些非刚性变换前后的图的距离计算具有较好的效果。
英文摘要：
Feature extraction and similarity measurement for graphs are important issues in computer vision and pattern recognition. However, traditional methods could not describe the graphs under some non-rigid transformation adequately, so a new graph feature descriptor and its similarity measurement method were proposed based on Graph Context （GC） descriptor. Firstly, a sample point set was obtained by discretely sampling. Secondly, graph context descriptor was presented based on the sample point set. At last, improved Earth Mover＇s Distance （EMD） was used to measure the similarity for graph context descriptor. Different from the graph edit distance methods, the proposed method did not need to define cost function which was difficult to set in those methods. The experimental results demonstrate that the proposed method performs better for the graphs under some non-rigid transformation.
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