We consider the problem of finding consistent matches across multiple images. Current state-
of-the-art solutions use constraints on cycles of matches together with convex optimization …

In this paper we propose a global optimization-based approach to jointly matching a set of
images. The estimated correspondences simultaneously maximize pairwise feature affinities …

Many computer vision applications require computing structure and feature correspondence
across a large, unorganized image collection. This is a computationally expensive process …

Matching between two sets of objects is typically approached by finding the object pairs that
collectively maximize the joint matching score. In this paper, we argue that this single …

Most existing approaches prune wrong matches via estimating an image transformation or
solving a graph-based global matching optimization problem, which usually suffers from …

We address two difficulties in establishing an accurate system for image matching. First,
image matching relies on the descriptor for feature extraction, but the optimal descriptor …

Deep learning-based image matching methods are improved significantly during the recent
years. Although these methods are reported to outperform the classical techniques, the …

Image matching plays a vital role in many computer vision tasks, and this paper focuses on
the mismatch removal problem of feature-based matching. We formulate the problem into a …

In this paper, we propose a mismatch removal method, which mines consistent image
feature correspondences using co-occurrence statistics. The proposed method relies on a …

Seeking reliable correspondences between two feature sets is a fundamental and important
task in computer vision. This paper attempts to remove mismatches from given putative …