Inexact graph matching using a hierarchy of matching processes

Abstract

Inexact graph matching algorithms have proved to be useful in many applications, such as character recognition, shape analysis, and image analysis. Inexact graph matching is, however, inherently an NP-hard problem with exponential computational complexity. Much of the previous research has focused on solving this problem using heuristics or estimations. Unfortunately, many of these techniques do not guarantee that an optimal solution will be found. It is the aim of the proposed algorithm to reduce the complexity of the inexact graph matching process, while still producing an optimal solution for a known application. This is achieved by greatly simplifying each individual matching process, and compensating for lost robustness by producing a hierarchy of matching processes. The creation of each matching process in the hierarchy is driven by an application-specific criterion that operates at the subgraph scale. To our knowledge, this problem has never before been approached in this manner. Results show that the proposed algorithm is faster than two existing methods based on graph edit operations. The proposed algorithm produces accurate results in terms of matching graphs, and shows promise for the application of shape matching. The proposed algorithm can easily be extended to produce a sub-optimal solution if required.

An Optimization Approach for Localization Refinement of Candidate Traffic Signs

“…As we know that the transformation between the two shapes is a homography, we finally fit a homography transformation between the two point sets using RANSAC [32]. An optional way to match shapes is to use graph matching [33] techniques. With this setting, the shape term is always weaker than the smoothness term, so segmentation is dominated by contrast.…”