Product graph-based higher order contextual similarities for inexact subgraph matching. (April 2018)

• Record Type: Journal Article
• Authors: Dutta, Anjan; Lladós, Josep; Bunke, Horst; Pal, Umapada
• Is Part Of: Pattern recognition. Volume 76(2018:Apr.)
• Page Start: 596
• Page End: 611
• Publication Date: 2018-04
• Subjects: Subgraph matching -- Product graph -- Random walks -- Backtrackless walks -- Contextual similarities -- Graphic recognition; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.12.003
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Contextual Similarities are proposed using Tensor Product Graph for graph matching. CS are obtained by diffusing the pairwise ones in the context of entire graph. Graph matching is formulated as a function of CS & solved with a linear programming. Well defined constraints are used to guide the node and edge selection procedure. Graphical abstract: Abstract: 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.