Conference Paper: Learning image-specific parameters for interactive segmentation
| Title | Learning image-specific parameters for interactive segmentation |
|---|---|
| Authors | Kuang, Z1 Schnieders, D1 Zhou, H1 Wong, KKY1 Yu, Y1 Peng, B2 |
| Keywords | Approximation scheme Conditional random field Cutting plane methods Energy margin Interactive image segmentation |
| Issue Date | 2012 |
| Publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147 |
| Citation | The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI., 16-21 June 2012. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2012, p. 590-597 [How to Cite?] DOI: http://dx.doi.org/10.1109/CVPR.2012.6247725 |
| Abstract | In this paper, we present a novel interactive image segmentation technique that automatically learns segmentation parameters tailored for each and every image. Unlike existing work, our method does not require any offline parameter tuning or training stage, and is capable of determining image-specific parameters according to some simple user interactions with the target image. We formulate the segmentation problem as an inference of a conditional random field (CRF) over a segmentation mask and the target image, and parametrize this CRF by different weights (e.g., color, texture and smoothing). The weight parameters are learned via an energy margin maximization, which is solved using a constraint approximation scheme and the cutting plane method. Experimental results show that our method, by learning image-specific parameters automatically, outperforms other state-of-the-art interactive image segmentation techniques. © 2012 IEEE. |
| Description | Posters 1B - Color and Texture, Early & Biological Vision, Image Based Modeling, Segmentation and Grouping |
| ISBN | 978-1-4673-1228-8 |
| ISSN | 1063-6919 2011 SCImago Journal Rankings: 0.044 |
| DOI | http://dx.doi.org/10.1109/CVPR.2012.6247725 |
| dc.contributor.author | Kuang, Z |
|---|---|
| dc.contributor.author | Schnieders, D |
| dc.contributor.author | Zhou, H |
| dc.contributor.author | Wong, KKY |
| dc.contributor.author | Yu, Y |
| dc.contributor.author | Peng, B |
| dc.date.accessioned | 2012-07-16T09:53:34Z |
| dc.date.available | 2012-07-16T09:53:34Z |
| dc.date.issued | 2012 |
| dc.description.abstract | In this paper, we present a novel interactive image segmentation technique that automatically learns segmentation parameters tailored for each and every image. Unlike existing work, our method does not require any offline parameter tuning or training stage, and is capable of determining image-specific parameters according to some simple user interactions with the target image. We formulate the segmentation problem as an inference of a conditional random field (CRF) over a segmentation mask and the target image, and parametrize this CRF by different weights (e.g., color, texture and smoothing). The weight parameters are learned via an energy margin maximization, which is solved using a constraint approximation scheme and the cutting plane method. Experimental results show that our method, by learning image-specific parameters automatically, outperforms other state-of-the-art interactive image segmentation techniques. © 2012 IEEE. |
| dc.description.nature | published_or_final_version |
| dc.description | Posters 1B - Color and Texture, Early & Biological Vision, Image Based Modeling, Segmentation and Grouping |
| dc.description.other | The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI., 16-21 June 2012. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2012, p. 590-597 |
| dc.identifier.citation | The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI., 16-21 June 2012. In IEEE Conference on Computer Vision and Pattern Recognition Proceedings, 2012, p. 590-597 [How to Cite?] DOI: http://dx.doi.org/10.1109/CVPR.2012.6247725 |
| dc.identifier.doi | http://dx.doi.org/10.1109/CVPR.2012.6247725 |
| dc.identifier.epage | 597 |
| dc.identifier.hkuros | 200763 |
| dc.identifier.isbn | 978-1-4673-1228-8 |
| dc.identifier.issn | 1063-6919 2011 SCImago Journal Rankings: 0.044 |
| dc.identifier.scopus | eid_2-s2.0-84866689544 |
| dc.identifier.spage | 590 |
| dc.identifier.uri | http://hdl.handle.net/10722/152969 |
| dc.language | eng |
| dc.publisher | IEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147 |
| dc.publisher.place | United States |
| dc.relation.ispartof | IEEE Conference on Computer Vision and Pattern Recognition Proceedings |
| dc.rights | IEEE Conference on Computer Vision and Pattern Recognition. Proceedings. Copyright © IEEE Computer Society. |
| dc.rights | ©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
| dc.rights | Creative Commons: Attribution 3.0 Hong Kong License |
| dc.subject | Approximation scheme |
| dc.subject | Conditional random field |
| dc.subject | Cutting plane methods |
| dc.subject | Energy margin |
| dc.subject | Interactive image segmentation |
| dc.title | Learning image-specific parameters for interactive segmentation |
| dc.type | Conference_Paper |
Author Affiliations
- The University of Hong Kong
- Hong Kong Polytechnic University