{\mathbf{x}_{i}}-{\mathbf{x}_{j}} \|}_{2}}} \right)},$$ where $a_{ij}\in{\mathbf{A}}$ is the affinity between $c_i$ and $c_j$. Second, the principle eigenvector $\mathbf{v}_{st}$ of $\mathbf{A}$ is computed using the singular value decomposition algorithm. Third, the maximum element in $\mathbf{v}_{st}$ is selected as ${v}_{i}$ indicating ${c}_{i}$ being the most reliable correspondence. Fourth, set $v_i$ to zero and remove other components of $\mathcal{C}$ that are in conflict with ${c}_{i}$, i.e., $$\label{eq:LRF6}
{{{a}_{ij}}\le t_{st}},$$ where $t_{st}$ is a predefined threshold. By repeating the third and fourth steps until $\mathcal{C}$ is empty or ${v}_{i}=0$, the correspondences related to all elements selected from $\mathbf{v}_{st}$ are determined as inliers.

**Game Theory Matching [@albarelli2012imposing].** GTM concentrates on extracting correspondences being consistent to the majority of $\mathcal{C}$. Specifically, this strategy interprets the filtering process as a game-theoretic framework where players attempt to obtain high payoffs. At the beginning of this game, every two players extracted from a large population choose a pair of correspondences (served as strategies in this context) from $\mathcal{C}$. Then they will receive a payoff linearly correlated to the coherence between these correspondences. The player who gets high payoffs will receive higher supports. In general, as the game going on, players will prefer to select more reliable correspondences to pursue higher 