Standard
Match graph construction for large image databases. /
Kim, Kwang In; Tompkin, James; Theobald, Martin et al.
Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I. Springer, 2012. p. 272-285 (Lecture Notes in Computer Science; Vol. 7572).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Kim, KI, Tompkin, J, Theobald, M, Kautz, J & Theobalt, C 2012,
Match graph construction for large image databases. in
Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I. Lecture Notes in Computer Science, vol. 7572, Springer, pp. 272-285.
https://doi.org/10.1007/978-3-642-33718-5_20
APA
Kim, K. I., Tompkin, J., Theobald, M., Kautz, J., & Theobalt, C. (2012).
Match graph construction for large image databases. In
Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I (pp. 272-285). (Lecture Notes in Computer Science; Vol. 7572). Springer.
https://doi.org/10.1007/978-3-642-33718-5_20
Vancouver
Kim KI, Tompkin J, Theobald M, Kautz J, Theobalt C.
Match graph construction for large image databases. In Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I. Springer. 2012. p. 272-285. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-33718-5_20
Author
Kim, Kwang In ; Tompkin, James ; Theobald, Martin et al. /
Match graph construction for large image databases. Computer Vision – ECCV 2012 : 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I. Springer, 2012. pp. 272-285 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{c83e54a00ca04806bb996dc2a79ee8de,
title = "Match graph construction for large image databases",
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.",
keywords = "Image matching, graph construction, link prediction",
author = "Kim, {Kwang In} and James Tompkin and Martin Theobald and Jan Kautz and Christian Theobalt",
year = "2012",
doi = "10.1007/978-3-642-33718-5_20",
language = "English",
isbn = "9783642337178",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "272--285",
booktitle = "Computer Vision – ECCV 2012",
}
RIS
TY - GEN
T1 - Match graph construction for large image databases
AU - Kim, Kwang In
AU - Tompkin, James
AU - Theobald, Martin
AU - Kautz, Jan
AU - Theobalt, Christian
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - Image matching
KW - graph construction
KW - link prediction
U2 - 10.1007/978-3-642-33718-5_20
DO - 10.1007/978-3-642-33718-5_20
M3 - Conference contribution/Paper
SN - 9783642337178
T3 - Lecture Notes in Computer Science
SP - 272
EP - 285
BT - Computer Vision – ECCV 2012
PB - Springer
ER -
