Product graph-based higher order contextual similarities for inexact subgraph matching

PATTERN RECOGNITION

卷:76

Article

Dutta, Anjan¹  Llados, Josep¹  Bunke, Horst²  Pal, Umapada³ 

[1] Autonomous Univ Barcelona, Comp Vis Ctr, Edifici 0,Campus UAB, Barcelona 08193, Spain

[2] Univ Bern, Inst Comp Sci, Neubriickstr 10, CH-3012 Bern, Switzerland

[3] Indian Stat Inst, Comp Vis & Pattern Recognit Unit, 203 BT Road, Kolkata 108, India

关键词: Subgraph matching;    Product graph;    Random walks;    Backtrackless walks;    Contextual similarities;    Graphic recognition;   

DOI  :  10.1016/j.patcog.2017.12.003

来源: Elsevier

【 摘 要 】

Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem. (C) 2017 Elsevier Ltd. All rights reserved.

【 授权许可】

Free