Elastic Net Constraint-Based Tensor Model for High-Order Graph Matching

The procedure of establishing the correspondence between two sets of feature points is important in computer vision applications. In this article, an elastic net constraint-based tensor model is proposed for high-order graph matching. To control the tradeoff between the sparsity and the accuracy of the matching results, an elastic net constraint is introduced into the tensor-based graph matching model. Then, a nonmonotone spectral projected gradient (NSPG) method is derived to solve the proposed matching model. During the optimization of using NSPG, we propose an algorithm to calculate the projection on the feasible convex sets of elastic net constraint. Further, the global convergence of solving the proposed model using the NSPG method was proved. The superiority of the proposed method is verified through experiments on the synthetic data and natural images.

Automated summary

The paper proposed an elastic net constraint-based tensor model for high-order graph matching, introducing a tradeoff between sparsity and accuracy. A nonmonotone spectral projected gradient method was derived for optimization, proving global convergence and superiority of the method through experiments.