Description: Graph matching (GM) is a fundamental problem in computer science, and it plays a central role to solve correspondence
problems in computer vision. GM problems that incorporate pairwise constraints can be formulated as a quadratic assignment problem
(QAP). Although widely used, solving the correspondence problem through GM has two main limitations: (1) the QAP is NP-hard and
difficult to approximate (2) GM algorithms do not incorporate geometric constraints between nodes that are natural in computer vision
problems. To address aforementioned problems, this paper proposes factorized graph matching (FGM). FGM factorizes the large
pairwise affinity matrix into smaller matrices that encode the local structure of each graph and the pairwise affinity between edges.
Four are the benefits that follow this factorization: (1) There is no need to compute the costly (in space and time) pairwise affinity
matrix (2) The factorization allows the use of a path-following optimization algorithm
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