Please use this identifier to cite or link to this item:
|Title:||Perspective motion segmentation via collaborative clustering|
|Citation:||Li, Z., Guo, J., Cheong, L.-F., Zhou, S.Z. (2013). Perspective motion segmentation via collaborative clustering. Proceedings of the IEEE International Conference on Computer Vision : 1369-1376. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCV.2013.173|
|Abstract:||This paper addresses real-world challenges in the motion segmentation problem, including perspective effects, missing data, and unknown number of motions. It first formulates the 3-D motion segmentation from two perspective views as a subspace clustering problem, utilizing the epipolar constraint of an image pair. It then combines the point correspondence information across multiple image frames via a collaborative clustering step, in which tight integration is achieved via a mixed norm optimization scheme. For model selection, we propose an over-segment and merge approach, where the merging step is based on the property of the ell-1-norm of the mutual sparse representation of two over-segmented groups. The resulting algorithm can deal with incomplete trajectories and perspective effects substantially better than state-of-the-art two-frame and multi-frame methods. Experiments on a 62-clip dataset show the significant superiority of the proposed idea in both segmentation accuracy and model selection. © 2013 IEEE.|
|Source Title:||Proceedings of the IEEE International Conference on Computer Vision|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 15, 2019
WEB OF SCIENCETM
checked on Jan 30, 2019
checked on Dec 15, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.