Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/609551
Title: Identity recognition based on generalised linear regression classification for multi-component images
Authors: Yang-Ting Chou;Jar-Ferr Yang
subject: FEI|colour principal component analysis|GLRC|generalised linear regression classification|multicomponent image classification|colour identify recognition|SOIL-47|identity image recognition|SRC|Columbia Object Image Library-100|colour canonical correlation analysis|SDUMLA-HMT|global adaptive weighted optimisation|colour linear discriminant analysis|sparse representation classification
Year: 2016
Publisher: IEEE
Abstract: In real-world recognition applications, several poor situations such as varying environment, limited image information, and irregular status would lead performance degradation in recognition. To overcome the unexpected effects, the authors propose a generalised linear regression classification (GLRC) to fully use all the information of multiple components of input images. The proposed GLRC achieves the global adaptive weighted optimisation for linear regression classification (LCR), which can automatically use the distinction components for recognition. For colour identify recognition, the authors also suggest several similarity measures for the proposed GLRC to be tested in different colour spaces. Experiments are conducted on two object datasets and two face databases including Columbia Object Image Library-100, SOIL-47, SDUMLA-HMT and FEI. For performance comparisons, the proposed GLRC approach is compared with the contemporary popular methods including colour principal component analysis, colour linear discriminant analysis, colour canonical correlation analysis, LRC, robust LRC (RLRC), sparse representation classification (SRC), colour LRC, colour RLRC, and colour SRC. The simulation results demonstrate that the proposed GLRC method achieves the best performance in multi-component identity recognition.
URI: http://localhost/handle/Hannan/141214
http://localhost/handle/Hannan/609551
ISSN: 1751-9632
1751-9640
volume: 10
issue: 1
Appears in Collections:2016

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Title: Identity recognition based on generalised linear regression classification for multi-component images
Authors: Yang-Ting Chou;Jar-Ferr Yang
subject: FEI|colour principal component analysis|GLRC|generalised linear regression classification|multicomponent image classification|colour identify recognition|SOIL-47|identity image recognition|SRC|Columbia Object Image Library-100|colour canonical correlation analysis|SDUMLA-HMT|global adaptive weighted optimisation|colour linear discriminant analysis|sparse representation classification
Year: 2016
Publisher: IEEE
Abstract: In real-world recognition applications, several poor situations such as varying environment, limited image information, and irregular status would lead performance degradation in recognition. To overcome the unexpected effects, the authors propose a generalised linear regression classification (GLRC) to fully use all the information of multiple components of input images. The proposed GLRC achieves the global adaptive weighted optimisation for linear regression classification (LCR), which can automatically use the distinction components for recognition. For colour identify recognition, the authors also suggest several similarity measures for the proposed GLRC to be tested in different colour spaces. Experiments are conducted on two object datasets and two face databases including Columbia Object Image Library-100, SOIL-47, SDUMLA-HMT and FEI. For performance comparisons, the proposed GLRC approach is compared with the contemporary popular methods including colour principal component analysis, colour linear discriminant analysis, colour canonical correlation analysis, LRC, robust LRC (RLRC), sparse representation classification (SRC), colour LRC, colour RLRC, and colour SRC. The simulation results demonstrate that the proposed GLRC method achieves the best performance in multi-component identity recognition.
URI: http://localhost/handle/Hannan/141214
http://localhost/handle/Hannan/609551
ISSN: 1751-9632
1751-9640
volume: 10
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7383402.pdf834.44 kBAdobe PDFThumbnail
Preview File
Title: Identity recognition based on generalised linear regression classification for multi-component images
Authors: Yang-Ting Chou;Jar-Ferr Yang
subject: FEI|colour principal component analysis|GLRC|generalised linear regression classification|multicomponent image classification|colour identify recognition|SOIL-47|identity image recognition|SRC|Columbia Object Image Library-100|colour canonical correlation analysis|SDUMLA-HMT|global adaptive weighted optimisation|colour linear discriminant analysis|sparse representation classification
Year: 2016
Publisher: IEEE
Abstract: In real-world recognition applications, several poor situations such as varying environment, limited image information, and irregular status would lead performance degradation in recognition. To overcome the unexpected effects, the authors propose a generalised linear regression classification (GLRC) to fully use all the information of multiple components of input images. The proposed GLRC achieves the global adaptive weighted optimisation for linear regression classification (LCR), which can automatically use the distinction components for recognition. For colour identify recognition, the authors also suggest several similarity measures for the proposed GLRC to be tested in different colour spaces. Experiments are conducted on two object datasets and two face databases including Columbia Object Image Library-100, SOIL-47, SDUMLA-HMT and FEI. For performance comparisons, the proposed GLRC approach is compared with the contemporary popular methods including colour principal component analysis, colour linear discriminant analysis, colour canonical correlation analysis, LRC, robust LRC (RLRC), sparse representation classification (SRC), colour LRC, colour RLRC, and colour SRC. The simulation results demonstrate that the proposed GLRC method achieves the best performance in multi-component identity recognition.
URI: http://localhost/handle/Hannan/141214
http://localhost/handle/Hannan/609551
ISSN: 1751-9632
1751-9640
volume: 10
issue: 1
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7383402.pdf834.44 kBAdobe PDFThumbnail
Preview File