Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCV.2011.6126323
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dc.titleMulti-label visual classification with label exclusive context
dc.contributor.authorChen, X.
dc.contributor.authorYuan, X.-T.
dc.contributor.authorChen, Q.
dc.contributor.authorYan, S.
dc.contributor.authorChua, T.-S.
dc.date.accessioned2013-07-23T09:31:05Z
dc.date.available2013-07-23T09:31:05Z
dc.date.issued2011
dc.identifier.citationChen, X.,Yuan, X.-T.,Chen, Q.,Yan, S.,Chua, T.-S. (2011). Multi-label visual classification with label exclusive context. Proceedings of the IEEE International Conference on Computer Vision : 834-841. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/ICCV.2011.6126323" target="_blank">https://doi.org/10.1109/ICCV.2011.6126323</a>
dc.identifier.isbn9781457711015
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/43327
dc.description.abstractWe introduce in this paper a novel approach to multi-label image classification which incorporates a new type of context label exclusive context with linear representation and classification. Given a set of exclusive label groups that describe the negative relationship among class labels, our method, namely LELR for Label Exclusive Linear Representation, enforces repulsive assignment of the labels from each group to a query image. The problem can be formulated as an exclusive Lasso (eLasso) model with group overlaps and affine transformation. Since existing eLasso solvers are not directly applicable to solving such an variant of eLasso in our setting, we propose a Nesterov's smoothing approximation algorithm for efficient optimization. Extensive comparing experiments on the challenging real-world visual classification benchmarks demonstrate the effectiveness of incorporating label exclusive context into visual classification. © 2011 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICCV.2011.6126323
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.departmentCOMPUTER SCIENCE
dc.description.doi10.1109/ICCV.2011.6126323
dc.description.sourcetitleProceedings of the IEEE International Conference on Computer Vision
dc.description.page834-841
dc.description.codenPICVE
dc.identifier.isiutNOT_IN_WOS
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