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Open AccessArticle

Multi-Label Classification Based on Low Rank Representation for Image Annotation

by 1, 2, 1 and 1,*
1
College of Computer and Information Science, Southwest University, Chongqing 400715, China
2
College of Hanhong, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(2), 109; https://doi.org/10.3390/rs9020109
Received: 7 November 2016 / Accepted: 22 January 2017 / Published: 27 January 2017
Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images. View Full-Text
Keywords: remote sensing images; image annotation; multi-label classification; low-rank representation; graph construction; semantic graph remote sensing images; image annotation; multi-label classification; low-rank representation; graph construction; semantic graph
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MDPI and ACS Style

Tan, Q.; Liu, Y.; Chen, X.; Yu, G. Multi-Label Classification Based on Low Rank Representation for Image Annotation. Remote Sens. 2017, 9, 109. https://doi.org/10.3390/rs9020109

AMA Style

Tan Q, Liu Y, Chen X, Yu G. Multi-Label Classification Based on Low Rank Representation for Image Annotation. Remote Sensing. 2017; 9(2):109. https://doi.org/10.3390/rs9020109

Chicago/Turabian Style

Tan, Qiaoyu; Liu, Yezi; Chen, Xia; Yu, Guoxian. 2017. "Multi-Label Classification Based on Low Rank Representation for Image Annotation" Remote Sens. 9, no. 2: 109. https://doi.org/10.3390/rs9020109

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