Transposed Low Rank Representation for Image Classification

Geoffrey Bull; Junbin Gao

doi:10.1109/DICTA.2012.6411718

Transposed Low Rank Representation for Image Classification

Geoffrey Bull, Junbin Gao

Computing and Mathematics

Research output: Book chapter/Published conference paper › Conference paper › peer-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 language	English
Title of host publication	Proceedings of the 2012 International Conference on Digital Image Computing: Techniques and Applications
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1-7
Number of pages	7
ISBN (Electronic)	9781467321815
DOIs	https://doi.org/10.1109/DICTA.2012.6411718
Publication status	Published - 2012
Event	2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Esplanade Hotel, Fremantle, Australia Duration: 03 Dec 2012 → 05 Dec 2012

Conference

Conference	2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
Country	Australia
City	Fremantle
Period	03/12/12 → 05/12/12
Other	The 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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title = "Transposed Low Rank Representation for Image Classification",

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.",

keywords = "Classification, Face recognition, Feature extraction, Hand written digit recognition, Low rank representation, MNIST",

author = "Geoffrey Bull and Junbin Gao",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2012. Event dates (773o) = 3-5th December 2012; Parent title (773t) = Digital Image Computing Techniques and Applications.; 2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA) ; Conference date: 03-12-2012 Through 05-12-2012",

year = "2012",

doi = "10.1109/DICTA.2012.6411718",

language = "English",

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booktitle = "Proceedings of the 2012 International Conference on Digital Image Computing: Techniques and Applications",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

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Bull, G & Gao, J 2012, Transposed Low Rank Representation for Image Classification. in Proceedings of the 2012 International Conference on Digital Image Computing: Techniques and Applications. IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1-7, 2012 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Fremantle, Australia, 03/12/12. https://doi.org/10.1109/DICTA.2012.6411718

Transposed Low Rank Representation for Image Classification. / Bull, Geoffrey; Gao, Junbin.

Proceedings of the 2012 International Conference on Digital Image Computing: Techniques and Applications. United States : IEEE, Institute of Electrical and Electronics Engineers, 2012. p. 1-7.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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KW - Face recognition

KW - Feature extraction

KW - Hand written digit recognition

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