Next Article in Journal
Limitation of SAR Quasi-Linear Inversion Data on Swell Climate: An Example of Global Crossing Swells
Previous Article in Journal
Potential and Limitation of SPOT-5 Ortho-Image Correlation to Investigate the Cinematics of Landslides: The Example of “Mare à Poule d’Eau” (Réunion, France)
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(2), 109; doi:10.3390/rs9020109

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

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.
Received: 7 November 2016 / Accepted: 22 January 2017 / Published: 27 January 2017
View Full-Text   |   Download PDF [1075 KB, uploaded 6 February 2017]   |  

Abstract

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
Figures

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top