Robust feature matching by learning descriptor covariance with viewpoint synthesis
Hajime Taira, Akihiko Torii, Masatoshi Okutomi · 2016
For images taken from very different viewpoints, we propose a new feature matching algorithm that provides accurate matches while preserving high matchability. Our method first synthesizes images by simulating the viewpoint changes. It then learns variation of local feature descriptors induced by the viewpoint changes. Finally, we robustly match feature descriptors by measuring the similarity using the learned variation. Our method is particularly useful for matching new query images to target image archived in a database. We demonstrate the benefits of the proposed method in terms of accuracy and computational time through experiments using several wide-baseline image datasets.