We present an alternate formulation of the partial assignment problem as matching random clique complexes, that are higher-order analogues of random graphs, designed to provide a set of invariants that better detect higher-order structure. The proposed method creates random clique adjacency matrices for each k-skeleton of the random clique complexes and matches them, taking into account each point as the affine combination of its geometric neighborhood. We justify our solution theoretically, by analyzing the runtime and storage complexity of our algorithm along with the asymptotic behavior of the quadratic assignment problem (QAP) that is associated with the underlying random clique adjacency matrices. Experiments on both synthetic and real-world datasets, containing severe occlusions and distortions, provide insight into the accuracy, efficiency, and robustness of our approach. We outperform diverse matching algorithms by a significant margin.
Charu Sharma (Indian Institute of Technology Hyderabad)
Deepak Nathani (IIT Hyderabad)
Manu Kaul (IIT Hyderabad)
Related Events (a corresponding poster, oral, or spotlight)
2018 Oral: Solving Partial Assignment Problems using Random Clique Complexes »
Fri Jul 13th 07:30 -- 07:50 AM Room K1