Conference item
Obj cut
- Abstract:
- In this paper we present a principled Bayesian method for detecting and segmenting instances of a particular object category within an image, providing a coherent methodology for combining top down and bottom up cues. The work draws together two powerful formulations: pictorial structures ( PS ) and Markov random fields (MRFs) both of which have efficient algorithms for their solution. The resulting combination, which we call the Object Category Specific MRF, suggests a solution to the problem that has long dogged MRFs namely that they provide a poor prior for specific shapes. In contrast, our model provides a prior that is global across the image plane using the PS. We develop an efficient method, OBJ CUT, to obtain segmentations using this model. Novel aspects of this method include an efficient algorithm for sampling the PS model, and the observation that the expected log likelihood of the model can be increased by a single graph cut. Results are presented on two object categories, cows and horses. We compare our methods to the state of the art in object category specific image segmentation and demonstrate significant improvements.
- Publication status:
- Published
- Peer review status:
- Reviewed (other)
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 923.8KB, Terms of use)
-
- Publisher copy:
- 10.1109/cvpr.2005.249
Authors
- Publisher:
- IEEE Computer Society
- Host title:
- Proceedings : 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition : CVPR 2005
- Place of publication:
- Los Alamitos, California, US
- Publication date:
- 2005-07-25
- Event title:
- 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
- Event series:
- Conference on Computer Vision and Pattern Recognition (CVPR)
- Event location:
- San Diego, CA, USA
- Event start date:
- 2005-06-20
- Event end date:
- 2005-06-25
- DOI:
- ISSN:
-
1063-6919
- ISBN-10:
- 0769523722
- ISBN-13:
- 9780769523729
- Language:
-
English
- Keywords:
- Pubs id:
-
61878
- Local pid:
-
pubs:61878
- Deposit date:
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2024-06-06
- ARK identifier:
Terms of use
- Copyright holder:
- Institute of Electrical and Electronics Engineers
- Copyright date:
- 2005
- Rights statement:
- © 2005 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
- Notes:
- This is the accepted manuscript version of the article. The final version is available from IEEE at: 10.1109/CVPR.2005.249
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