Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation
AbstractAnnotating 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
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Tan, Q.; Liu, Y.; Chen, X.; Yu, G. Multi-Label Classiﬁcation Based on Low Rank Representation for Image Annotation. Remote Sens. 2017, 9, 109.
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.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.
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