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Author, Editor

Author(s):

Kim, Kwang In
Tompkin, James
Theobald, Martin
Kautz, Jan
Theobalt, Christian

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Not MPG Author(s):

Kautz, Jan

Editor(s):

Fitzgibbon, Andrew W.
Lazebnik, Svetlana
Perona, Pietro
Sato, Yoichi
Schmid, Cordelia

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BibTeX cite key*:

Kim2012

Title, Booktitle

Title*:

Match Graph Construction for Large Image Databases

Booktitle*:

12th European Conference on Computer Vision

Event, URLs

URL of the conference:

http://eccv2012.unifi.it/

URL for downloading the paper:


Event Address*:

Florence, Italy

Language:

English

Event Date*
(no longer used):


Organization:


Event Start Date:

7 October 2012

Event End Date:

13 October 2012

Publisher

Name*:

Springer

URL:


Address*:

New York

Type:


Vol, No, Year, pp.

Series:

Lecture Notes in Computer Science

Volume:

7572

Number:


Month:


Pages:

272-285

Year*:

2012

VG Wort Pages:


ISBN/ISSN:

978-3-642-33711-6

Sequence Number:


DOI:




Note, Abstract, ©


(LaTeX) 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.



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Access Level:

Internal

Correlation

MPG Unit:

Max-Planck-Institut für Informatik



MPG Subunit:

Computer Graphics Group

Audience:

experts only

Appearance:

MPII WWW Server, MPII FTP Server, MPG publications list, university publications list, working group publication list, Fachbeirat, VG Wort



BibTeX Entry:

@INPROCEEDINGS{Kim2012,
AUTHOR = {Kim, Kwang In and Tompkin, James and Theobald, Martin and Kautz, Jan and Theobalt, Christian},
EDITOR = {Fitzgibbon, Andrew W. and Lazebnik, Svetlana and Perona, Pietro and Sato, Yoichi and Schmid, Cordelia},
TITLE = {Match Graph Construction for Large Image Databases},
BOOKTITLE = {12th European Conference on Computer Vision},
PUBLISHER = {Springer},
YEAR = {2012},
VOLUME = {7572},
PAGES = {272--285},
SERIES = {Lecture Notes in Computer Science},
ADDRESS = {Florence, Italy},
ISBN = {978-3-642-33711-6},
}


Entry last modified by James Tompkin, 02/05/2013
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Editor(s)
James Tompkin
Created
12/17/2012 02:37:10 PM
Revision
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Editor
James Tompkin
James Tompkin


Edit Date
02/05/2013 07:59:16 PM
17/12/2012 14:37:10