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...

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Bibliographic Details
Published in:2008 IEEE Conference on Computer Vision and Pattern Recognition pp. 1 - 8
Main Authors: Kohli, P., Ladicky, L., Torr, P.
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2008
Subjects:
Image generation
Image resolution
Image segmentation
Inference algorithms
Labeling
Object segmentation
Pixel
Robustness
Stereo image processing
Testing
ISBN:9781424422425, 1424422426
ISSN:1063-6919, 1063-6919
Online Access:Get full text
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Summary: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 P n 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.
ISBN:9781424422425
1424422426
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2008.4587417