Joint matching over a collection of objects aims at aggregating information from a large
collection of similar instances (eg images, graphs, shapes) to improve maps between pairs …

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 …

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 …

Entity resolution (ER) is the process of identifying records that refer to the same entities
within one or across multiple databases. Numerous techniques have been developed to …

The matching of multiple objects (eg shapes or images) is a fundamental problem in vision
and graphics. In order to robustly handle ambiguities, noise and repetitive patterns in …

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 …

For robust feature matching, a popular and particularly effective method is to recover smooth
functions from the data to differentiate the true correspondences (inliers) from false …

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 …

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 …

This paper presents a framework of layered graph matching for integrating graph partition
and matching. The objective is to find an unknown number of corresponding graph …