Progressive Large Scale-Invariant Image Matching in Scale Space
Lei Zhou, Siyu Zhu, Tianwei Shen, Jinglu Wang, Tian Fang, Long Quan
Abstract
The power of modern image matching approaches is still fundamentally limited by the abrupt scale changes in images. In this paper, we propose a scale-invariant image matching approach to tackling the very large scale variation of views. Drawing inspiration from the scale space theory, we start with encoding the image's scale space into a compact multi-scale representation. Then, rather than trying to find the exact feature matches all in one step, we propose a progressive two-stage approach. First, we determine the related scale levels in scale space, enclosing the inlier feature correspondences, based on an optimal and exhaustive matching in a limited scale space. Second, we produce both the image similarity measurement and feature correspondences simultaneously after restricting matching between the related scale levels in a robust way. The matching performance has been intensively evaluated on vision tasks including image retrieval, feature matching and Structure-from-Motion (SfM). The successful integration of the challenging fusion of high aerial and low ground-level views with significant scale differences manifests the superiority of the proposed approach.
BibTeX
@inproceedings{iccv2017_progressivelarge,
title = {Progressive Large Scale-Invariant Image Matching in Scale Space},
author = {Lei Zhou and Siyu Zhu and Tianwei Shen and Jinglu Wang and Tian Fang and Long Quan},
booktitle = {ICCV 2017},
year = {2017}
}