Zero-Assignment Constraint for Graph Matching with Outliers
This addresses a practical issue in computer vision and pattern recognition where outliers degrade graph matching performance, offering an incremental improvement for robust matching.
The paper tackles the problem of graph matching with numerous outliers by introducing a zero-assignment constraint to suppress outlier matchings, resulting in a state-of-the-art method that improves accuracy and efficiency in experiments.
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.