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Remote Sens. 2019, 11(8), 922; https://doi.org/10.3390/rs11080922

Exploring Weighted Dual Graph Regularized Non-Negative Matrix Tri-Factorization Based Collaborative Filtering Framework for Multi-Label Annotation of Remote Sensing Images

1
Xi’an Microelectronics Technology Institute, Xi’an 710068, China
2
School of Software Engineering, Xi’an Jiaotong University, Xi’an 710049, China
*
Author to whom correspondence should be addressed.
Received: 14 March 2019 / Revised: 8 April 2019 / Accepted: 12 April 2019 / Published: 16 April 2019
(This article belongs to the Special Issue Image Retrieval in Remote Sensing)
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Abstract

Manually annotating remote sensing images is laborious work, especially on large-scale datasets. To improve the efficiency of this work, we propose an automatic annotation method for remote sensing images. The proposed method formulates the multi-label annotation task as a recommended problem, based on non-negative matrix tri-factorization (NMTF). The labels of remote sensing images can be recommended directly by recovering the image–label matrix. To learn more efficient latent feature matrices, two graph regularization terms are added to NMTF that explore the affiliated relationships on the image graph and label graph simultaneously. In order to reduce the gap between semantic concepts and visual content, both low-level visual features and high-level semantic features are exploited to construct the image graph. Meanwhile, label co-occurrence information is used to build the label graph, which discovers the semantic meaning to enhance the label prediction for unlabeled images. By employing the information from images and labels, the proposed method can efficiently deal with the sparsity and cold-start problem brought by limited image–label pairs. Experimental results on the UCMerced and Corel5k datasets show that our model outperforms most baseline algorithms for multi-label annotation of remote sensing images and performs efficiently on large-scale unlabeled datasets. View Full-Text
Keywords: multi-label annotation; remote sensing imagery; collaborative filtering; non-negative matrix tri-factorization multi-label annotation; remote sensing imagery; collaborative filtering; non-negative matrix tri-factorization
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, J.; Zhang, J.; Dai, T.; He, Z. Exploring Weighted Dual Graph Regularized Non-Negative Matrix Tri-Factorization Based Collaborative Filtering Framework for Multi-Label Annotation of Remote Sensing Images. Remote Sens. 2019, 11, 922.

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