{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:04:32Z","timestamp":1753887872185,"version":"3.41.2"},"reference-count":32,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T00:00:00Z","timestamp":1615593600000},"content-version":"vor","delay-in-days":71,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005089","name":"Beijing Municipal Natural Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002888","name":"Beijing Municipal Commission of Education","doi-asserted-by":"publisher","award":["PXM2019 014213 000007"],"award-info":[{"award-number":["PXM2019 014213 000007"]}],"id":[{"id":"10.13039\/501100002888","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61877002"],"award-info":[{"award-number":["61877002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Wireless Communications and Mobile Computing"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, extracting incomplete labels through a pretrained CNN, and (2) label rectification, rectifying the generated incomplete labels by the KELM to obtain the rectified labels. Experiments conducted on publicly available datasets demonstrate the effectiveness of our method. Notably, our method is extensible which can be easily integrated with off\u2010the\u2010shelf networks for improving performance.<\/jats:p>","DOI":"10.1155\/2021\/6669081","type":"journal-article","created":{"date-parts":[[2021,3,13]],"date-time":"2021-03-13T18:20:10Z","timestamp":1615659610000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Label Rectification Learning through Kernel Extreme Learning Machine"],"prefix":"10.1155","volume":"2021","author":[{"given":"Qiang","family":"Cai","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1744-206X","authenticated-orcid":false,"given":"Fenghai","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yifan","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Haisheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Shanshan","family":"Li","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,3,13]]},"reference":[{"key":"e_1_2_10_1_2","doi-asserted-by":"crossref","unstructured":"DengJ. GuoJ. XueN. andZafeiriouS. Additive angular margin loss for deep face recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition June 2019 California 4690\u20134699.","DOI":"10.1109\/CVPR.2019.00482"},{"key":"e_1_2_10_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.07.009"},{"key":"e_1_2_10_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.08.008"},{"key":"e_1_2_10_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.12.013"},{"key":"e_1_2_10_5_2","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13568"},{"key":"e_1_2_10_6_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015981"},{"key":"e_1_2_10_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107619"},{"key":"e_1_2_10_8_2","doi-asserted-by":"crossref","unstructured":"LiX. WangW. HuX. andYangJ. Selective kernel networks Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) June 2019 California 510\u2013519.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"e_1_2_10_9_2","doi-asserted-by":"crossref","unstructured":"DalalN.andTriggsB. Histograms of oriented gradients for human detection IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u203205) June 2005 San Diego CA USA https:\/\/doi.org\/10.1109\/CVPR.2005.177 2-s2.0-33645146449.","DOI":"10.1109\/CVPR.2005.177"},{"key":"e_1_2_10_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_2_10_11_2","unstructured":"IandolaF. N. HanS. MoskewiczM. W. AshrafK. DallyW. J. andKeutzerK. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and\u00a1 0.5 mb model size 2016 http:\/\/arxiv.org\/abs\/1602.07360."},{"key":"e_1_2_10_12_2","first-page":"1150","article-title":"Object recognition from local scale-invariant features","volume":"99","author":"Lowe D. G.","year":"1999","journal-title":"ICCV"},{"key":"e_1_2_10_13_2","unstructured":"SutskeverI. VinyalsO. andLeQ. Sequence to sequence learning with neural networks 2014 http:\/\/arxiv.org\/abs\/1409.3215."},{"key":"e_1_2_10_14_2","unstructured":"GriffinG. HolubA. andPeronaP. Caltech-256 object category dataset 2007 Technical Report 7694 California Institute of Technology Pasadena."},{"key":"e_1_2_10_15_2","doi-asserted-by":"crossref","unstructured":"HuangG. LiuZ. Van Der MaatenL. andWeinbergerK. Q. Densely connected convolutional networks Proceedings of the IEEE conference on computer vision and pattern recognition 2017 Hawaii America 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_2_10_16_2","unstructured":"GaoS. H. ChengM. M. ZhaoK. ZhangX. Y. YangM. H. andTorrP. Res2net: a new multi-scale backbone architecture 2019 http:\/\/arxiv.org\/abs\/1904.01169."},{"key":"e_1_2_10_17_2","unstructured":"AgarapA. F. An architecture combining convolutional neural network (cnn) and support vector machine (svm) for image classification 2017 http:\/\/arxiv.org\/abs\/1712.03541."},{"key":"e_1_2_10_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2018.11.020"},{"key":"e_1_2_10_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2005.12.126"},{"key":"e_1_2_10_20_2","doi-asserted-by":"crossref","unstructured":"LiZ. ZhuX. WangL. andGuoP. Image classification using convolutional neural networks and kernel extreme learning machines 2018 25th IEEE International Conference on Image Processing (ICIP) October 2018 Athens Greece 3009\u20133013 https:\/\/doi.org\/10.1109\/ICIP.2018.8451560 2-s2.0-85062921074.","DOI":"10.1109\/ICIP.2018.8451560"},{"key":"e_1_2_10_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2011.2168604"},{"key":"e_1_2_10_22_2","unstructured":"SimonyanK.andZissermanA. Very deep convolutional networks for large-scale image recognition 2014 http:\/\/arxiv.org\/abs\/1409.1556."},{"key":"e_1_2_10_23_2","doi-asserted-by":"crossref","unstructured":"SzegedyC. LiuW. JiaY. SermanetP. ReedS. AnguelovD. ErhanD. VanhouckeV. andRabinovichA. Going deeper with convolutions Proceedings of the IEEE conference on computer vision and pattern recognition. 2015 Boston America 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_2_10_24_2","unstructured":"LinM. ChenQ. andYanS. Network in network 2013 http:\/\/arxiv.org\/abs\/1312.4400."},{"key":"e_1_2_10_25_2","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"27","author":"Krizhevsky A.","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"e_1_2_10_26_2","unstructured":"KrizhevskyA.andHintonG. Learning multiple layers of features from tiny images 2009 Technical Report TR-2009 University of Toronto Toronto."},{"key":"e_1_2_10_27_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep residual learning for image recognition Proceedings of the IEEE conference on computer vision and pattern recognition 2016 Las Vegas America 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_10_28_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33018425"},{"key":"e_1_2_10_29_2","unstructured":"LarssonG. MaireM. andShakhnarovichG. Fractalnet: ultra-deep neural networks without residuals 2016 http:\/\/arxiv.org\/abs\/1605.07648."},{"key":"e_1_2_10_30_2","unstructured":"IoffeS.andSzegedyC. Batch normalization: accelerating deep network training by reducing internal covariate shift 2015 http:\/\/arxiv.org\/abs\/1502.03167."},{"key":"e_1_2_10_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"e_1_2_10_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_39"}],"container-title":["Wireless Communications and Mobile Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/6669081.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/wcmc\/2021\/6669081.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2021\/6669081","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T09:35:09Z","timestamp":1723023309000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2021\/6669081"}},"subtitle":[],"editor":[{"given":"Zhili","family":"Zhou","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":32,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["10.1155\/2021\/6669081"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6669081","archive":["Portico"],"relation":{},"ISSN":["1530-8669","1530-8677"],"issn-type":[{"type":"print","value":"1530-8669"},{"type":"electronic","value":"1530-8677"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"2020-12-02","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-02-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-03-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"6669081"}}