Abstract
A novel algorithm to segment a primary object in a video sequence is proposed in this work. First, we generate candidate regions for the primary object using both color and motion edges. Second, we estimate initial primary object regions, by exploiting the recurrence property of the primary object. Third, we augment the initial regions with missing parts or reducing them by excluding noisy parts repeatedly. This augmentation and reduction process (ARP) identifies the primary object region in each frame. Experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-Art conventional algorithms on recent benchmark datasets.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 7417-7425 |
| Number of pages | 9 |
| Volume | 2017-January |
| ISBN (Electronic) | 9781538604571 |
| DOIs | |
| Publication status | Published - 2017 Nov 6 |
| Event | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 - Honolulu, United States Duration: 2017 Jul 21 → 2017 Jul 26 |
Other
| Other | 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 |
|---|---|
| Country | United States |
| City | Honolulu |
| Period | 17/7/21 → 17/7/26 |
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition
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Cite this
Koh, Y. J., & Kim, C-S. (2017). Primary object segmentation in videos based on region augmentation and reduction. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (Vol. 2017-January, pp. 7417-7425). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CVPR.2017.784