Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2010.5539967
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dc.titleVisual classification with multi-task joint sparse representation
dc.contributor.authorYuan, X.-T.
dc.contributor.authorYan, S.
dc.date.accessioned2014-06-19T03:32:16Z
dc.date.available2014-06-19T03:32:16Z
dc.date.issued2010
dc.identifier.citationYuan, X.-T., Yan, S. (2010). Visual classification with multi-task joint sparse representation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 3493-3500. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2010.5539967
dc.identifier.isbn9781424469840
dc.identifier.issn10636919
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/72171
dc.description.abstractWe address the problem of computing joint sparse representation of visual signal across multiple kernel-based representations. Such a problem arises naturally in supervised visual recognition applications where one aims to reconstruct a test sample with multiple features from as few training subjects as possible. We cast the linear version of this problem into a multi-task joint covariate selection model [15], which can be very efficiently optimized via kernelizable accelerated proximal gradient method. Furthermore, two kernel-view extensions of this method are provided to handle the situations where descriptors and similarity functions are in the form of kernel matrices. We then investigate into two applications of our algorithm to feature combination: 1) fusing gray-level and LBP features for face recognition, and 2) combining multiple kernels for object categorization. Experimental results on challenging real-world datasets show that the feature combination capability of our proposed algorithm is competitive to the state-of-theart multiple kernel learning methods. ©2010 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CVPR.2010.5539967
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CVPR.2010.5539967
dc.description.sourcetitleProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
dc.description.page3493-3500
dc.description.codenPIVRE
dc.identifier.isiut000287417503070
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