Point pattern matching based on line graph spectral context and descriptor embedding

Spectral methods have been extensively studied for point pattern matching. In this work, we aim to render the spectral matching algorithm more robust for positional jitter and outliers. We concentrate on the issue of spectral representation for point patterns. A local structural descriptor, called the line graph spectral context, is proposed to characterize the attribute of point patterns, making it fundamentally different from the available representation approaches at the global level. For any given point, we first construct a line graph using its neighboring points. Then the eigenvalues of various matrix representations associated with the obtained line graph are used as the point descriptor. Furthermore, the similarities between the descriptors are evaluated by comparing their low dimensional embedding via the technique of multiview spectral embedding. The proposed descriptor is finally integrated with a graph-matching framework for establishing the correspondences. Comparative experiments conducted on both synthetic data and real-world images show the effectiveness of the proposed method, especially in the presence of positional jitter and outliers.