Progressive Large Scale-Invariant Image Matching in Scale Space

Lei Zhou, Siyu Zhu, Tianwei Shen, Jinglu Wang, Tian Fang, Long Quan; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2362-2371

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.

Related Material


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[bibtex]
@InProceedings{Zhou_2017_ICCV,
author = {Zhou, Lei and Zhu, Siyu and Shen, Tianwei and Wang, Jinglu and Fang, Tian and Quan, Long},
title = {Progressive Large Scale-Invariant Image Matching in Scale Space},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}