Robust higher order potentials for enforcing label consistency
This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
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IEEE
2008
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| _version_ | 1826315030826582016 |
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| author | Kohli, P Ladický, L Torr, PHS |
| author_facet | Kohli, P Ladický, L Torr, PHS |
| author_sort | Kohli, P |
| collection | OXFORD |
| description | This paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation algorithms. These potentials enforce label consistency in image regions and can be seen as a strict generalization of the commonly used pairwise contrast sensitive smoothness potentials. The higher order potential functions used in our framework take the form of the Robust Pn model. This enables the use of powerful graph cut based move making algorithms for performing inference in the framework [14]. We test our method on the problem of multi-class object segmentation by augmenting the conventional CRF used for object segmentation with higher order potentials defined on image regions. Experiments on challenging data sets show that integration of higher order potentials quantitatively and qualitatively improves results leading to much better definition of object boundaries. We believe that this method can be used to yield similar improvements for many other labelling problems. |
| first_indexed | 2024-12-09T03:18:34Z |
| format | Conference item |
| id | oxford-uuid:74b37400-5337-4cca-a4e6-7b34a31de0c1 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-12-09T03:18:34Z |
| publishDate | 2008 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:74b37400-5337-4cca-a4e6-7b34a31de0c12024-10-30T12:34:29ZRobust higher order potentials for enforcing label consistencyConference itemhttp://purl.org/coar/resource_type/c_5794uuid:74b37400-5337-4cca-a4e6-7b34a31de0c1EnglishSymplectic ElementsIEEE2008Kohli, PLadický, LTorr, PHSThis paper proposes a novel framework for labelling problems which is able to combine multiple segmentations in a principled manner. Our method is based on higher order conditional random fields and uses potentials defined on sets of pixels (image segments) generated using unsupervised segmentation algorithms. These potentials enforce label consistency in image regions and can be seen as a strict generalization of the commonly used pairwise contrast sensitive smoothness potentials. The higher order potential functions used in our framework take the form of the Robust Pn model. This enables the use of powerful graph cut based move making algorithms for performing inference in the framework [14]. We test our method on the problem of multi-class object segmentation by augmenting the conventional CRF used for object segmentation with higher order potentials defined on image regions. Experiments on challenging data sets show that integration of higher order potentials quantitatively and qualitatively improves results leading to much better definition of object boundaries. We believe that this method can be used to yield similar improvements for many other labelling problems. |
| spellingShingle | Kohli, P Ladický, L Torr, PHS Robust higher order potentials for enforcing label consistency |
| title | Robust higher order potentials for enforcing label consistency |
| title_full | Robust higher order potentials for enforcing label consistency |
| title_fullStr | Robust higher order potentials for enforcing label consistency |
| title_full_unstemmed | Robust higher order potentials for enforcing label consistency |
| title_short | Robust higher order potentials for enforcing label consistency |
| title_sort | robust higher order potentials for enforcing label consistency |
| work_keys_str_mv | AT kohlip robusthigherorderpotentialsforenforcinglabelconsistency AT ladickyl robusthigherorderpotentialsforenforcinglabelconsistency AT torrphs robusthigherorderpotentialsforenforcinglabelconsistency |