Graph-based multiple rank regression for image classification
Yuan, Haoliang; Li, Junyu; Lai, Loi Lei; Tang, Yuan Yan
Source PublicationNEUROCOMPUTING
AbstractImage classification is one important task in image processing and pattern recognition. Traditional image classification methods commonly transform the image into a vector. However, in essence, image is a matrix data and using vector instead of image loses the correlations of the matrix data. To address this problem, we propose a graph-based multiple rank regression model (GMRR), which employs multiple-rank left and right projecting vectors to regress each matrix data to its label for each category. To exploit the discriminating structure of the data space, a class compactness graph is constructed to constrain these left and right projecting vectors. The extensive experimental results on image classification have demonstrated the effectiveness of our proposed method. (c) 2018 Elsevier B.V. All rights reserved.
KeywordMultiple rank regression Graph regularization Image classification
URLView the original
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000445934400036
The Source to ArticleWOS
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Yuan, Haoliang,Li, Junyu,Lai, Loi Lei,et al. Graph-based multiple rank regression for image classification[J]. NEUROCOMPUTING,2018,315:394-404.
APA Yuan, Haoliang,Li, Junyu,Lai, Loi Lei,&Tang, Yuan Yan.(2018).Graph-based multiple rank regression for image classification.NEUROCOMPUTING,315,394-404.
MLA Yuan, Haoliang,et al."Graph-based multiple rank regression for image classification".NEUROCOMPUTING 315(2018):394-404.
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