Transposed Low Rank Representation for Image Classification

Geoffrey Bull, Junbin Gao

Research output: Book chapter/Published conference paperConference paperpeer-review

9 Citations (Scopus)

Abstract

This paper proposes a method for supervised classificationusing Low-Rank Representation of transposed data.Recent papers have suggested that low rank representationof transposed data may be useful for feature extraction. Wedevelop an algorithm called TLRRC for supervised classificationusing transposed data and demonstrate that its performance iscompetitive with state-of-the-art classification methods.
Original languageEnglish
Title of host publicationProceedings of the 2012 International Conference on Digital Image Computing: Techniques and Applications
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-7
Number of pages7
ISBN (Electronic)9781467321815
DOIs
Publication statusPublished - 2012
Event2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Esplanade Hotel, Fremantle, Australia
Duration: 03 Dec 201205 Dec 2012

Conference

Conference2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
CountryAustralia
CityFremantle
Period03/12/1205/12/12
OtherThe International Conference on Digital Image Computing: Techniques and Applications (DICTA) is the main Australian Conference on computer vision, image processing, pattern recognition, and related areas. DICTA was established as a biannual conference in 1991 and became an annual event in 2007. It is the premiere conference of the Australian Pattern Recognition Society (APRS).

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