Conference Paper: Markov Weight Fields for face sketch synthesis
| Title | Markov Weight Fields for face sketch synthesis |
|---|---|
| Authors | Zhou, H1 Kuang, Z1 Wong, KKY1 |
| Keywords | Convex quadratic programming Decomposition methods Face sketch synthesis Markov property Markov random fields models |
| 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. 1091-1097 [How to Cite?] DOI: http://dx.doi.org/10.1109/CVPR.2012.6247788 |
| Abstract | Great progress has been made in face sketch synthesis in recent years. State-of-the-art methods commonly apply a Markov Random Fields (MRF) model to select local sketch patches from a set of training data. Such methods, however, have two major drawbacks. Firstly, the MRF model used cannot synthesize new sketch patches. Secondly, the optimization problem in solving the MRF is NP-hard. In this paper, we propose a novel Markov Weight Fields (MWF) model that is capable of synthesizing new sketch patches. We formulate our model into a convex quadratic programming (QP) problem to which the optimal solution is guaranteed. Based on the Markov property of our model, we further propose a cascade decomposition method (CDM) for solving such a large scale QP problem efficiently. Experimental results on the CUHK face sketch database and celebrity photos show that our model outperforms the common MRF model used in other state-of-the-art methods. © 2012 IEEE. |
| Description | Posters 1C - Vision for Graphics, Sensors, Medical, Vision for Robotics, Applications |
| ISBN | 978-1-4673-1228-8 |
| ISSN | 1063-6919 2011 SCImago Journal Rankings: 0.044 |
| DOI | http://dx.doi.org/10.1109/CVPR.2012.6247788 |
| dc.contributor.author | Zhou, H |
|---|---|
| dc.contributor.author | Kuang, Z |
| dc.contributor.author | Wong, KKY |
| dc.date.accessioned | 2012-08-16T06:03:10Z |
| dc.date.available | 2012-08-16T06:03:10Z |
| dc.date.issued | 2012 |
| dc.description.abstract | Great progress has been made in face sketch synthesis in recent years. State-of-the-art methods commonly apply a Markov Random Fields (MRF) model to select local sketch patches from a set of training data. Such methods, however, have two major drawbacks. Firstly, the MRF model used cannot synthesize new sketch patches. Secondly, the optimization problem in solving the MRF is NP-hard. In this paper, we propose a novel Markov Weight Fields (MWF) model that is capable of synthesizing new sketch patches. We formulate our model into a convex quadratic programming (QP) problem to which the optimal solution is guaranteed. Based on the Markov property of our model, we further propose a cascade decomposition method (CDM) for solving such a large scale QP problem efficiently. Experimental results on the CUHK face sketch database and celebrity photos show that our model outperforms the common MRF model used in other state-of-the-art methods. © 2012 IEEE. |
| dc.description.nature | published_or_final_version |
| dc.description | Posters 1C - Vision for Graphics, Sensors, Medical, Vision for Robotics, Applications |
| 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. 1091-1097 |
| 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. 1091-1097 [How to Cite?] DOI: http://dx.doi.org/10.1109/CVPR.2012.6247788 |
| dc.identifier.doi | http://dx.doi.org/10.1109/CVPR.2012.6247788 |
| dc.identifier.epage | 1097 |
| dc.identifier.hkuros | 203486 |
| dc.identifier.isbn | 978-1-4673-1228-8 |
| dc.identifier.issn | 1063-6919 2011 SCImago Journal Rankings: 0.044 |
| dc.identifier.scopus | eid_2-s2.0-84866686477 |
| dc.identifier.spage | 1091 |
| dc.identifier.uri | http://hdl.handle.net/10722/160098 |
| 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 | Convex quadratic programming |
| dc.subject | Decomposition methods |
| dc.subject | Face sketch synthesis |
| dc.subject | Markov property |
| dc.subject | Markov random fields models |
| dc.title | Markov Weight Fields for face sketch synthesis |
| dc.type | Conference_Paper |
Author Affiliations
- The University of Hong Kong