Visual Homing via Guided Locality Preserving Matching

This study proposes a simple yet surprisingly effective feature matching approach, termed as guided locality preserving matching (GLPM), for visual homing of panoramic images. The key idea of our approach is merely to preserve the neighborhood structures of potential true matches between two panoramic images. We formulate it into a mathematical model, and derive a simple closed-form solution with linearithmic time and linear space complexities. This enables our method to accomplish the mismatch removal from hundreds of putative correspondences in only a few milliseconds. To handle extremely large proportions of outliers, we further design a guided matching strategy based on the proposed method, using the matching result on a small putative set with a high inlier ratio to guide the matching on a large putative set. This strategy can also significantly boost true matches without sacrifice in accuracy. To apply our GLPM to the visual homing problem, we develop a method for dense motion flow estimation from sparse feature matches based on Tikhonov regularization. Moreover, the focus-of-contraction/focus-of-expansion is derived to determine homing directions. The effectiveness of our method is demonstrated on a panoramic database in both feature matching and visual homing.