Abstract
We propose a novel approach to unsupervised video segmentation based on the trajectories of Temporal Super-pixels (TSPs). We cast the segmentation problem as a trajectory-labeling problem and define a Markov random field on a graph in which each node represents a trajectory of TSPs, which we minimize using a new two-stage optimization method we developed. The adaption of the trajectories as basic building blocks brings several advantages over conventional superpixel-based methods, such as more expressive potential functions, temporal coherence of the resulting segmentation, and drastically reduced number of the MRF nodes. The most important effect is, however, that it allows more robust segmentation of the foreground that is static in some frames. The method is evaluated on a subset of the standard SegTrack benchmark and yields competitive results against the state-of-the-art methods.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 448-451 |
| Number of pages | 4 |
| ISBN (Electronic) | 9784901122160 |
| DOIs | |
| Publication status | Published - 2017 Jul 19 |
| Event | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan Duration: 2017 May 8 → 2017 May 12 |
Other
| Other | 15th IAPR International Conference on Machine Vision Applications, MVA 2017 |
|---|---|
| Country | Japan |
| City | Nagoya |
| Period | 17/5/8 → 17/5/12 |
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ASJC Scopus subject areas
- Computer Science Applications
- Computer Vision and Pattern Recognition
Cite this
Unsupervised video object segmentation by supertrajectory labeling. / Masuda, Masahiro; Mochizuki, Yoshihiko; Ishikawa, Hiroshi.
Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 448-451 7986897.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Unsupervised video object segmentation by supertrajectory labeling
AU - Masuda, Masahiro
AU - Mochizuki, Yoshihiko
AU - Ishikawa, Hiroshi
PY - 2017/7/19
Y1 - 2017/7/19
N2 - We propose a novel approach to unsupervised video segmentation based on the trajectories of Temporal Super-pixels (TSPs). We cast the segmentation problem as a trajectory-labeling problem and define a Markov random field on a graph in which each node represents a trajectory of TSPs, which we minimize using a new two-stage optimization method we developed. The adaption of the trajectories as basic building blocks brings several advantages over conventional superpixel-based methods, such as more expressive potential functions, temporal coherence of the resulting segmentation, and drastically reduced number of the MRF nodes. The most important effect is, however, that it allows more robust segmentation of the foreground that is static in some frames. The method is evaluated on a subset of the standard SegTrack benchmark and yields competitive results against the state-of-the-art methods.
AB - We propose a novel approach to unsupervised video segmentation based on the trajectories of Temporal Super-pixels (TSPs). We cast the segmentation problem as a trajectory-labeling problem and define a Markov random field on a graph in which each node represents a trajectory of TSPs, which we minimize using a new two-stage optimization method we developed. The adaption of the trajectories as basic building blocks brings several advantages over conventional superpixel-based methods, such as more expressive potential functions, temporal coherence of the resulting segmentation, and drastically reduced number of the MRF nodes. The most important effect is, however, that it allows more robust segmentation of the foreground that is static in some frames. The method is evaluated on a subset of the standard SegTrack benchmark and yields competitive results against the state-of-the-art methods.
UR - http://www.scopus.com/inward/record.url?scp=85027851736&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85027851736&partnerID=8YFLogxK
U2 - 10.23919/MVA.2017.7986897
DO - 10.23919/MVA.2017.7986897
M3 - Conference contribution
AN - SCOPUS:85027851736
SP - 448
EP - 451
BT - Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -