Video object segmentation by integrating trajectories from points and regions
| LEADER | caa a22 4500 | ||
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| 001 | 605446954 | ||
| 003 | CHVBK | ||
| 005 | 20210128100129.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s11042-014-2145-5 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s11042-014-2145-5 | ||
| 245 | 0 | 0 | |a Video object segmentation by integrating trajectories from points and regions |h [Elektronische Daten] |c [Geng Zhang, Zejian Yuan, Yuehu Liu, Liang Ma, Nanning Zheng] |
| 520 | 3 | |a We describe a novel video object segmentation system based on a conditional random field model with high-order term which is capable of capturing longer-range spatial and temporal grouping information. Our system is able to segment different moving objects effectively from complex background due to integrating the complementary properties of trajectories from points and regions. Although point and region trajectories have already been used in video object segmentation, their complementary properties have not been well investigated. In this paper, we propose an ingenious scheme to transfer the labels of sparse point trajectories to region trajectories. Especially, for region trajectories with few texture, this scheme can automatically predict their label probabilities by using a Gaussian mixture model of appearance and motion given the labels of point trajectories. Meanwhile, we design a reliability measurement for region trajectories based on shape consistency, which helps us to design robust high-order potentials for spatially overlapping region trajectories. Our region trajectories are extracted from hierarchical image over-segmentation, and hence they can capture meaningful regions over time. Additionally, our approach is a streaming process, in which object labels are propagated over a video. We validate the effectiveness of our approach on public challenging datasets, and show that our approach outperforms other competing methods | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a Video object segmentation |2 nationallicence | |
| 690 | 7 | |a Point tajectory |2 nationallicence | |
| 690 | 7 | |a Region trajectory |2 nationallicence | |
| 690 | 7 | |a Complementary property |2 nationallicence | |
| 690 | 7 | |a High-order model |2 nationallicence | |
| 700 | 1 | |a Zhang |D Geng |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | |
| 700 | 1 | |a Yuan |D Zejian |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | |
| 700 | 1 | |a Liu |D Yuehu |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | |
| 700 | 1 | |a Ma |D Liang |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | |
| 700 | 1 | |a Zheng |D Nanning |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | |
| 773 | 0 | |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/21(2015-11-01), 9665-9696 |x 1380-7501 |q 74:21<9665 |1 2015 |2 74 |o 11042 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s11042-014-2145-5 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s11042-014-2145-5 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zhang |D Geng |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Yuan |D Zejian |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Liu |D Yuehu |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ma |D Liang |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Zheng |D Nanning |u Institute of Artificial Intelligence and Robotics, #28 West Xianning Road, Xi'an, Shaanxi, People's Republic of China |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Multimedia Tools and Applications |d Springer US; http://www.springer-ny.com |g 74/21(2015-11-01), 9665-9696 |x 1380-7501 |q 74:21<9665 |1 2015 |2 74 |o 11042 | ||