Towards Accurate Image Matching by Exploring Redundancy Between Multiple Descriptors

Abstract

Finding correspondences between a pair of images is the key ingredient for many applications such as localization and panorama. However, due to a variety of challenges between multi-view images in practice, the results of using a single kind of descriptor may vary significantly across different scenes. In this paper, we treat the assignment task as a clustering problem and propose an image matching method that fuses multiple descriptors to tackle the above difficulties. First, we extract multiple descriptors at the keypoints on two images. Then, we compute a pairwise similarity matrix for each kind of descriptor. Afterwards, we compute a weighted combination of these similarity matrices, and use it to build correspondences via a modified multi-kernel clustering module. The proposed method is tested on three public image datasets: two ground image sets and an Unmanned Aerial Vehicle (UAV) image set. Experiments show that the proposed method can adapt to different number of descriptors. It significantly improves the matching accuracy in a variety of scenarios and downstream tasks.