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Conference Paper: Markov Weight Fields for face sketch synthesis

TitleMarkov Weight Fields for face sketch synthesis
Authors
KeywordsConvex quadratic programming
Decomposition methods
Face sketch synthesis
Markov property
Markov random fields models
Issue Date2012
PublisherIEEE 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?
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.
DescriptionPosters 1C - Vision for Graphics, Sensors, Medical, Vision for Robotics, Applications
Persistent Identifierhttp://hdl.handle.net/10722/160098
ISBN
ISSN
2013 SCImago Journal Rankings: 2.954

 

Author Affiliations
  1. The University of Hong Kong
DC FieldValueLanguage
dc.contributor.authorZhou, Hen_US
dc.contributor.authorKuang, Zen_US
dc.contributor.authorWong, KKYen_US
dc.date.accessioned2012-08-16T06:03:10Z-
dc.date.available2012-08-16T06:03:10Z-
dc.date.issued2012en_US
dc.identifier.citationThe 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. 1091-1097en_US
dc.identifier.isbn978-1-4673-1228-8-
dc.identifier.issn1063-6919-
dc.identifier.urihttp://hdl.handle.net/10722/160098-
dc.descriptionPosters 1C - Vision for Graphics, Sensors, Medical, Vision for Robotics, Applications-
dc.description.abstractGreat 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.languageengen_US
dc.publisherIEEE Computer Society. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000147-
dc.relation.ispartofIEEE Conference on Computer Vision and Pattern Recognition Proceedingsen_US
dc.rightsIEEE 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectConvex quadratic programming-
dc.subjectDecomposition methods-
dc.subjectFace sketch synthesis-
dc.subjectMarkov property-
dc.subjectMarkov random fields models-
dc.titleMarkov Weight Fields for face sketch synthesisen_US
dc.typeConference_Paperen_US
dc.identifier.emailZhou, H: hzhou@cs.hku.hken_US
dc.identifier.emailKuang, Z: zhkuang@cs.hku.hk-
dc.identifier.emailWong, KKY: kykwong@cs.hku.hk-
dc.identifier.authorityWong, KKY=rp01393en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CVPR.2012.6247788-
dc.identifier.scopuseid_2-s2.0-84866686477-
dc.identifier.hkuros203486en_US
dc.identifier.spage1091en_US
dc.identifier.epage1097en_US
dc.publisher.placeUnited States-
dc.description.otherThe 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-

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