Improved Subspace Clustering via Exploitation of Spatial Constraints
We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sp...
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2012
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/4473 |
| _version_ | 1848744525600653312 |
|---|---|
| author | Pham, DucSon Budhaditya, Saha Phung, Dinh Venkatesh, Svetha |
| author2 | N/A |
| author_facet | N/A Pham, DucSon Budhaditya, Saha Phung, Dinh Venkatesh, Svetha |
| author_sort | Pham, DucSon |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso regression and suggest numerical algorithms so solve the proposed formulation. The experiments on the benchmark Johns Hopkins 155 dataset demonstrate that exploiting spatial constraints significantly improves motion segmentation. |
| first_indexed | 2025-11-14T06:02:51Z |
| format | Conference Paper |
| id | curtin-20.500.11937-4473 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:02:51Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-44732017-09-13T16:02:18Z Improved Subspace Clustering via Exploitation of Spatial Constraints Pham, DucSon Budhaditya, Saha Phung, Dinh Venkatesh, Svetha N/A subspace segmentation sparse representation clustering We present a novel approach to improving subspace clustering by exploiting the spatial constraints. The new method encourages the sparse solution to be consistent with the spatial geometry of the tracked points, by embedding weights into the sparse formulation. By doing so, we are able to correct sparse representations in a principled manner without introducing much additional computational cost. We discuss alternative ways to treat the missing and corrupted data using the latest theory in robust lasso regression and suggest numerical algorithms so solve the proposed formulation. The experiments on the benchmark Johns Hopkins 155 dataset demonstrate that exploiting spatial constraints significantly improves motion segmentation. 2012 Conference Paper http://hdl.handle.net/20.500.11937/4473 10.1109/CVPR.2012.6247720 IEEE restricted |
| spellingShingle | subspace segmentation sparse representation clustering Pham, DucSon Budhaditya, Saha Phung, Dinh Venkatesh, Svetha Improved Subspace Clustering via Exploitation of Spatial Constraints |
| title | Improved Subspace Clustering via Exploitation of Spatial Constraints |
| title_full | Improved Subspace Clustering via Exploitation of Spatial Constraints |
| title_fullStr | Improved Subspace Clustering via Exploitation of Spatial Constraints |
| title_full_unstemmed | Improved Subspace Clustering via Exploitation of Spatial Constraints |
| title_short | Improved Subspace Clustering via Exploitation of Spatial Constraints |
| title_sort | improved subspace clustering via exploitation of spatial constraints |
| topic | subspace segmentation sparse representation clustering |
| url | http://hdl.handle.net/20.500.11937/4473 |