Edge Feature Based Learning Complete Graph Matching Model

Traditional linear learning graph matching model is easy to be trained and can achieve a global optimal solution.However,this model doesn't consider the information of graph structure,thus limiting its matching accuracy.To overcome this disadvantage,we propose a novel linear learning graph matching model-edge feature based learning complete graph matching model(ELC-GM).An edge feature is constructed from its sampling point features,which are described by an extension of shape context with rotation invariant factors.After supervised training of ELC-GM,Kuhn-Munkres is used to solve the edge match and then Hungarian decoder is applied to determine the final point match.Experimental results show that ELC-GM can achieve good performances with improvement of accuracy,even in cases of deformation and noise.