Title :
Transposed Low Rank Representation for Image Classification
Author :
Bull, Geoff ; Junbin Gao
Author_Institution :
Sch. of Comput. & Math., Charles Sturt Univ., Bathurst, NSW, Australia
Abstract :
This paper proposes a method for supervised classification using Low-Rank Representation of transposed data. Recent papers have suggested that low rank representation of transposed data may be useful for feature extraction. We develop an algorithm called TLRRC for supervised classification using transposed data and demonstrate that its performance is competitive with state-of-the-art classification methods.
Keywords :
feature extraction; handwritten character recognition; image classification; image representation; learning (artificial intelligence); optical character recognition; TLRRC algorithm; feature extraction; supervised image classification method; transposed data low-rank representation; Face; Face recognition; Feature extraction; Principal component analysis; Support vector machines; Training; Training data;
Conference_Titel :
Digital Image Computing Techniques and Applications (DICTA), 2012 International Conference on
Conference_Location :
Fremantle, WA
Print_ISBN :
978-1-4673-2180-8
Electronic_ISBN :
978-1-4673-2179-2
DOI :
10.1109/DICTA.2012.6411718
