Home > Research > Publications & Outputs > Match graph construction for large image databases
View graph of relations

Match graph construction for large image databases

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Published
  • Kwang In Kim
  • James Tompkin
  • Martin Theobald
  • Jan Kautz
  • Christian Theobalt
Close
Publication date2012
Host publicationComputer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I
PublisherSpringer
Pages272-285
Number of pages14
ISBN (Electronic)9783642337185
ISBN (Print)9783642337178
Original languageEnglish

Publication series

NameLecture Notes in Computer Science
Volume7572
ISSN (Print)0302-9743

Abstract

How best to efficiently establish correspondence among a large set of images or video frames is an interesting unanswered question. For large databases, the high computational cost of performing pair-wise image matching is a major problem. However, for many applications, images are inherently sparsely connected, and so current techniques try to correctly estimate small potentially matching subsets of databases upon which to perform expensive pair-wise matching. Our contribution is to pose the identification of potential matches as a link prediction problem in an image correspondence graph, and to propose an effective algorithm to solve this problem. Our algorithm facilitates incremental image matching: initially, the match graph is very sparse, but it becomes dense as we alternate between link prediction and verification. We demonstrate the effectiveness of our algorithm by comparing it with several existing alternatives on large-scale databases. Our resulting match graph is useful for many different applications. As an example, we show the benefits of our graph construction method to a label propagation application which propagates user-provided sparse object labels to other instances of that object in large image collections.