Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Visual Computing |
| Subtitle of host publication | 10th International Symposium, ISVC 2014, Proceedings |
| Editors | George Bebis, Richard Boyle, Bahram Parvin, Darko Koracin, Ryan McMahan, Jason Jerald, Hui Zhang, Steven M. Drucker, Kambhamettu Chandra, El Choubassi Maha, Zhigang Deng, Mark Carlson |
| Publisher | Springer Verlag |
| Pages | 281-292 |
| Number of pages | 12 |
| ISBN (electronic) | 9783319142487 |
| Publication status | Published - 2014 |
| Event | 10th International Symposium on Visual Computing, ISVC 2014 - Las Vegas, United States Duration: 8 Dec 2014 → 10 Dec 2014 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 8887 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Interactive video segmentation has become a popular topic in computer vision and computer graphics. Discrete optimization using maximum flow algorithms is one of the preferred techniques to perform interactive video segmentation. This paper extends pixel based graph cut approaches to overcome the problem of high memory requirements. The basic idea is to use a graph cut optimization framework on top of temporally coherent superpixels. While grouping spatially coherent pixels sharing similar color, these algorithms additionally exploit the temporal connections between those image regions. Thereby the number of variables in the optimization framework is severely reduced. The effectiveness of the proposed algorithm is shown quantitatively, qualitatively and through timing comparisons of different temporally coherent superpixel approaches. Experiments on video sequences show that temporally coherent superpixels lead to significant speed-up and reduced memory consumption. Thus, video sequences can be interactively segmented in a more efficient manner while producing better segmentation quality when compared to other approaches.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
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Advances in Visual Computing : 10th International Symposium, ISVC 2014, Proceedings. ed. / George Bebis; Richard Boyle; Bahram Parvin; Darko Koracin; Ryan McMahan; Jason Jerald; Hui Zhang; Steven M. Drucker; Kambhamettu Chandra; El Choubassi Maha; Zhigang Deng; Mark Carlson. Springer Verlag, 2014. p. 281-292 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8887).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Interactive Segmentation of High-Resolution Video Content Using Temporally Coherent Superpixels and Graph Cut
AU - Reso, Matthias
AU - Scheuermann, Björn
AU - Jachalsky, Jörn
AU - Rosenhahn, Bodo
AU - Ostermann, Jörn
PY - 2014
Y1 - 2014
N2 - Interactive video segmentation has become a popular topic in computer vision and computer graphics. Discrete optimization using maximum flow algorithms is one of the preferred techniques to perform interactive video segmentation. This paper extends pixel based graph cut approaches to overcome the problem of high memory requirements. The basic idea is to use a graph cut optimization framework on top of temporally coherent superpixels. While grouping spatially coherent pixels sharing similar color, these algorithms additionally exploit the temporal connections between those image regions. Thereby the number of variables in the optimization framework is severely reduced. The effectiveness of the proposed algorithm is shown quantitatively, qualitatively and through timing comparisons of different temporally coherent superpixel approaches. Experiments on video sequences show that temporally coherent superpixels lead to significant speed-up and reduced memory consumption. Thus, video sequences can be interactively segmented in a more efficient manner while producing better segmentation quality when compared to other approaches.
AB - Interactive video segmentation has become a popular topic in computer vision and computer graphics. Discrete optimization using maximum flow algorithms is one of the preferred techniques to perform interactive video segmentation. This paper extends pixel based graph cut approaches to overcome the problem of high memory requirements. The basic idea is to use a graph cut optimization framework on top of temporally coherent superpixels. While grouping spatially coherent pixels sharing similar color, these algorithms additionally exploit the temporal connections between those image regions. Thereby the number of variables in the optimization framework is severely reduced. The effectiveness of the proposed algorithm is shown quantitatively, qualitatively and through timing comparisons of different temporally coherent superpixel approaches. Experiments on video sequences show that temporally coherent superpixels lead to significant speed-up and reduced memory consumption. Thus, video sequences can be interactively segmented in a more efficient manner while producing better segmentation quality when compared to other approaches.
UR - http://www.scopus.com/inward/record.url?scp=84916618690&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-14249-4_27
DO - 10.1007/978-3-319-14249-4_27
M3 - Conference contribution
AN - SCOPUS:84916618690
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 281
EP - 292
BT - Advances in Visual Computing
A2 - Bebis, George
A2 - Boyle, Richard
A2 - Parvin, Bahram
A2 - Koracin, Darko
A2 - McMahan, Ryan
A2 - Jerald, Jason
A2 - Zhang, Hui
A2 - Drucker, Steven M.
A2 - Chandra, Kambhamettu
A2 - Maha, El Choubassi
A2 - Deng, Zhigang
A2 - Carlson, Mark
PB - Springer Verlag
T2 - 10th International Symposium on Visual Computing, ISVC 2014
Y2 - 8 December 2014 through 10 December 2014
ER -