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dc.contributor.authorZhang, Tianzhu
dc.contributor.authorGhanem, Bernard
dc.contributor.authorLiu, Si
dc.contributor.authorXu, Changsheng
dc.contributor.authorAhuja, Narendra
dc.date.accessioned2015-06-02T14:04:00Z
dc.date.available2015-06-02T14:04:00Z
dc.date.issued2014-03-07
dc.identifier.citationZhang, T., Ghanem, B., Liu, S., Xu, C., & Ahuja, N. (2013). Low-Rank Sparse Coding for Image Classification. 2013 IEEE International Conference on Computer Vision. doi:10.1109/iccv.2013.42
dc.identifier.doi10.1109/ICCV.2013.42
dc.identifier.urihttp://hdl.handle.net/10754/556147
dc.description.abstractIn this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6751144
dc.relation.urlhttp://vcc.kaust.edu.sa/Documents/B.%20Ghanem/papers/LRSC_ICCV2013.pdf
dc.rights(c) 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectbow
dc.subjectimage classification
dc.subjectlow-rank
dc.titleLow-Rank Sparse Coding for Image Classification
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journal2013 IEEE International Conference on Computer Vision
dc.conference.date1 December 2013 through 8 December 2013
dc.conference.name2013 14th IEEE International Conference on Computer Vision, ICCV 2013
dc.conference.locationSydney, NSW
dc.eprint.versionPost-print
dc.contributor.institutionAdvanced Digital Sciences Center of Illinois, Singapore
dc.contributor.institutionNational University of Singapore, Singapore
dc.contributor.institutionInstitute of Automation, Chinese Academy of Sciences, P. R. China
dc.contributor.institutionUniversity of Illinois at Urbana-Champaign, Urbana, IL USA
kaust.personGhanem, Bernard
refterms.dateFOA2018-06-14T07:59:04Z
dc.date.published-online2014-03-07
dc.date.published-print2013-12


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