关键词: 相似性度量, 形状上下文, 直方图分析, 推土机距离, 主成分分析, 多维尺度分析

Abstract: 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.

Key words: similarity measurement, shape context, histogram analysis, Earth Mover's Distance (EMD), Principal Component Analysis (PCA), Multi-Dimensional Scaling (MDS) analysis