Title: Exploiting Multi-layer Graph Factorization for Multi-attributed Graph Matching,
Abstract: Multi-attributed graph matching is a problem of finding correspondences
between two sets of data while considering their complex properties described
in multiple attributes. However, the information of multiple attributes is
likely to be oversimplified during a process that makes an integrated
attribute, and this degrades the matching accuracy. For that reason, a
multi-layer graph structure-based algorithm has been proposed recently. It can
effectively avoid the problem by separating attributes into multiple layers.
Nonetheless, there are several remaining issues such as a scalability problem
caused by the huge matrix to describe the multi-layer structure and a
back-projection problem caused by the continuous relaxation of the quadratic
assignment problem. In this work, we propose a novel multi-attributed graph
matching algorithm based on the multi-layer graph factorization. We reformulate
the problem to be solved with several small matrices that are obtained by
factorizing the multi-layer structure. Then, we solve the problem using a
convex-concave relaxation procedure for the multi-layer structure. The proposed
algorithm exhibits better performance than state-of-the-art algorithms based on
the single-layer structure.