  Graph matching (GM), as a longstanding problem in computer vision and pattern
recognition, still suffers from numerous cluttered outliers in practical
applications. To address this issue, we present the zero-assignment constraint
(ZAC) for approaching the graph matching problem in the presence of outliers.
The underlying idea is to suppress the matchings of outliers by assigning
zero-valued vectors to the potential outliers in the obtained optimal
correspondence matrix. We provide elaborate theoretical analysis to the
problem, i.e., GM with ZAC, and figure out that the GM problem with and without
outliers are intrinsically different, which enables us to put forward a
sufficient condition to construct valid and reasonable objective function.
Consequently, we design an efficient outlier-robust algorithm to significantly
reduce the incorrect or redundant matchings caused by numerous outliers.
Extensive experiments demonstrate that our method can achieve the
state-of-the-art performance in terms of accuracy and efficiency, especially in
the presence of numerous outliers.
