针对传统复杂网络方法对形状的非刚性变形较为敏感等问题，在形状内部距离的基础上利用有向复杂网络进行形状分析。首先提取形状边界点作为复杂网络的节点，以形状边界点之间的内部距离作为复杂网络中节点之间的边权值构建初始网络；然后对初始复杂网络进行k近邻演化，得到不同演化时刻的有向子网络；最后提取各有向子网络的特征来实现复杂网络的特征描述，进而实现形状的特征表示。实验结果表明，该方法对常见的形状变形具有更强的鲁棒性；与传统的无向网络模型相比，具有更高的检索和分类精度。%For shape boundary becomes instable in some non‐rigid transformation and other issues on traditional undirected complex network models ,a new directed complex network model based on inner distances has been proposed to characteristic shape boundaries . Firstly , boundary points and inner distances between these points are represented as nodes and weights of edges of the initial network , respectively .Then ,this initial network evolved based on the k‐nearest neighbor method and its sub‐networks were generated at each evolution stage .Finally ,features of these directed sub‐networks are computed and concatenated to describe the shape boundary . Experimental results on both shape recognition and retrieval show that the proposed method can perform more robust than traditional undirected complex network models .
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