{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T10:38:38Z","timestamp":1778409518833,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,1,27]],"date-time":"2017-01-27T00:00:00Z","timestamp":1485475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61402378"],"award-info":[{"award-number":["61402378"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2014jcyjA40031"],"award-info":[{"award-number":["cstc2014jcyjA40031"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005230","name":"Natural Science Foundation of Chongqing","doi-asserted-by":"publisher","award":["cstc2016jcyjA0351"],"award-info":[{"award-number":["cstc2016jcyjA0351"]}],"id":[{"id":"10.13039\/501100005230","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/rs9020109","type":"journal-article","created":{"date-parts":[[2017,1,27]],"date-time":"2017-01-27T11:23:02Z","timestamp":1485516182000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Multi-Label Classi\ufb01cation Based on Low Rank Representation for Image Annotation"],"prefix":"10.3390","volume":"9","author":[{"given":"Qiaoyu","family":"Tan","sequence":"first","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yezi","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Hanhong, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xia","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1667-6705","authenticated-orcid":false,"given":"Guoxian","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Science, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/LGRS.2012.2237502","article-title":"Semantic annotation of high-resolution remote sensing images via Gaussian process multi-instance multilabel learning","volume":"10","author":"Chen","year":"2013","journal-title":"IEEE Geosci. 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