关键词: 图模式识别, 非精确图匹配, 空间句法, 最短路径, 图核

Abstract: In the field of graph-based pattern recognition, the existing graph kernels can’t mine the node features that reflect graph’s topology sufficiently. To solve this problem, the present study proposes new graph kernels based on space syntax and shortest path. This paper takes advantage of the space syntax theory in architecture and urban planning to make up quantitative description of the graph’s topological features and proposes the space syntax kernel and the space syntax kernel based on shortest path. These graph kernels are expressible, positive definite, computable and applicable to most graphs, and then are used to implement the inexact graph matching using SVM. Differ from other graph kernel methods, the proposed method can render the graph’s topology adequately and has a favorable universality. Experimental results show that such graph kernels can achieve superior results in classification compared to shortest path kernel.

Key words: graph-based pattern recognition, inexact graph matching, space syntax, shortest path, graph kernel