{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T15:20:13Z","timestamp":1773069613743,"version":"3.50.1"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2021,2,28]],"date-time":"2021-02-28T00:00:00Z","timestamp":1614470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61966004, 61663004, 61762078, 61866004, and 61876111"],"award-info":[{"award-number":["61966004, 61663004, 61762078, 61866004, and 61876111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"crossref","award":["2019GXNSFDA245018 and 2018GXNSFDA281009"],"award-info":[{"award-number":["2019GXNSFDA245018 and 2018GXNSFDA281009"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>To learn a well-performed image annotation model, a large number of labeled samples are usually required. Although the unlabeled samples are readily available and abundant, it is a difficult task for humans to annotate large numbers of images manually. In this article, we propose a novel semi-supervised approach based on adaptive weighted fusion for automatic image annotation that can simultaneously utilize the labeled data and unlabeled data to improve the annotation performance. At first, two different classifiers, constructed based on support vector machine and covolutional neural network, respectively, are trained by different features extracted from the labeled data. Therefore, these two classifiers are independently represented as different feature views. Then, the corresponding features of unlabeled images are extracted and input into these two classifiers, and the semantic annotation of images can be obtained respectively. At the same time, the confidence of corresponding image annotation can be measured by an adaptive weighted fusion strategy. After that, the images and its semantic annotations with high confidence are submitted to the classifiers for retraining until a certain stop condition is reached. As a result, we can obtain a strong classifier that can make full use of unlabeled data. Finally, we conduct experiments on four datasets, namely, Corel 5K, IAPR TC12, ESP Game, and NUS-WIDE. In addition, we measure the performance of our approach with standard criteria, including precision, recall, F-measure, N+, and mAP. The experimental results show that our approach has superior performance and outperforms many state-of-the-art approaches.<\/jats:p>","DOI":"10.1145\/3426974","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T12:42:08Z","timestamp":1618576928000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["A Semi-supervised Learning Approach Based on Adaptive Weighted Fusion for Automatic Image Annotation"],"prefix":"10.1145","volume":"17","author":[{"given":"Zhixin","family":"Li","sequence":"first","affiliation":[{"name":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, China"}]},{"given":"Lan","family":"Lin","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, China"}]},{"given":"Canlong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, China"}]},{"given":"Huifang","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Northwest Normal University, China"}]},{"given":"Weizhong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer, Central China Normal University, China"}]},{"given":"Zhiping","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Capital Normal University, China"}]}],"member":"320","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (SIGIR). ACM, 127\u2013134","author":"David","unstructured":"David M. Blei and Michael I. Jordan. 2003. Modeling annotated data . In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (SIGIR). ACM, 127\u2013134 . David M. Blei and Michael I. Jordan. 2003. Modeling annotated data. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (SIGIR). ACM, 127\u2013134."},{"key":"e_1_2_1_2_1","volume-title":"Jordan","author":"Blei David M.","year":"2003","unstructured":"David M. Blei , Andrew Y. Ng , and Michael I . Jordan . Jan. 2003 . Latent Dirichlet allocation. J. Mach. Learn. Res . 3 (Jan. 2003), 993\u20131022. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Jan. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3 (Jan. 2003), 993\u20131022."},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.61"},{"key":"e_1_2_1_5_1","volume-title":"Article 107164 (Apr.","author":"Cevikalp Hakan","year":"2020","unstructured":"Hakan Cevikalp , Burak Benligiray , and Omer Nezih Gerek . Apr. 2020. Semi-supervised robust deep neural networks for multi-label image classification. Pattern Recogn. 100 , Article 107164 (Apr. 2020 ), 9 pages. Hakan Cevikalp, Burak Benligiray, and Omer Nezih Gerek. Apr. 2020. Semi-supervised robust deep neural networks for multi-label image classification. Pattern Recogn. 100, Article 107164 (Apr. 2020), 9 pages."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1873951.1873959"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1646396.1646452"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1348246.1348248"},{"key":"e_1_2_1_10_1","volume-title":"Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (Jun","author":"Dem\u0161ar Janez","year":"2006","unstructured":"Janez Dem\u0161ar . Jun. 2006. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (Jun . 2006 ), 1\u201330. Janez Dem\u0161ar. Jun. 2006. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (Jun. 2006), 1\u201330."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_2_1_12_1","volume-title":"Proceedings of the European Conference on Computer Vision (ECCV\u201902)","author":"Duygulu Pinar","unstructured":"Pinar Duygulu , Kobus Barnard , Joao F. G. de Freitas , and David A. Forsyth . 2002. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary . In Proceedings of the European Conference on Computer Vision (ECCV\u201902) . Springer, 97\u2013112. Pinar Duygulu, Kobus Barnard, Joao F. G. de Freitas, and David A. Forsyth. 2002. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Proceedings of the European Conference on Computer Vision (ECCV\u201902). Springer, 97\u2013112."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2009.03.008"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.1315274"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.170"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the 17th International Conference on Machine Learning (ICML\u201900)","author":"Goldman Sally A","year":"2000","unstructured":"Sally A Goldman and Yan Zhou . 2000 . Enhancing supervised learning with unlabeled data . In Proceedings of the 17th International Conference on Machine Learning (ICML\u201900) . ACM, 327\u2013334. Sally A Goldman and Yan Zhou. 2000. Enhancing supervised learning with unlabeled data. In Proceedings of the 17th International Conference on Machine Learning (ICML\u201900). ACM, 327\u2013334."},{"key":"e_1_2_1_17_1","unstructured":"Yunchao Gong Yangqing Jia Thomas Leung Alexander Toshev and Sergey Ioffe. 2013. Deep convolutional ranking for multilabel image annotation. arXiv:1312.4894. Retrieved from https:\/\/arxiv.org\/abs\/1312.4894.  Yunchao Gong Yangqing Jia Thomas Leung Alexander Toshev and Sergey Ioffe. 2013. Deep convolutional ranking for multilabel image annotation. arXiv:1312.4894. Retrieved from https:\/\/arxiv.org\/abs\/1312.4894."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2009.5459266"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007617005950"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/860435.860459"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2719939"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2549459"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.525"},{"key":"e_1_2_1_25_1","volume-title":"Data equilibrium based automatic image annotation by fusing deep model and semantic propagation. Pattern Recogn. 71 (Nov","author":"Ke Xiao","year":"2017","unstructured":"Xiao Ke , Mingke Zhou , Yuzhen Niu , and Wenzhong Guo . Nov. 2017. Data equilibrium based automatic image annotation by fusing deep model and semantic propagation. Pattern Recogn. 71 (Nov . 2017 ), 60\u201377. Xiao Ke, Mingke Zhou, Yuzhen Niu, and Wenzhong Guo. Nov. 2017. Data equilibrium based automatic image annotation by fusing deep model and semantic propagation. Pattern Recogn. 71 (Nov. 2017), 60\u201377."},{"key":"e_1_2_1_26_1","volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS\u201912)","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky , Ilya Sutskever , and Geoffrey E. Hinton . 2012. ImageNet classification with deep convolutional neural networks . In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS\u201912) . MIT Press, 1106\u20131114. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS\u201912). MIT Press, 1106\u20131114."},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS\u201904)","author":"Lavrenko Victor","year":"2004","unstructured":"Victor Lavrenko , Raghavan Manmatha , and Jiwoon Jeon . 2004 . A model for learning the semantics of pictures . In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS\u201904) . MIT Press, 553\u2013560. Victor Lavrenko, Raghavan Manmatha, and Jiwoon Jeon. 2004. A model for learning the semantics of pictures. In Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS\u201904). MIT Press, 553\u2013560."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-016-0530-9"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3323873.3325023"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2013.03.016"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2010.11.015"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2013.07.004"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-010-0338-6"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.1097"},{"key":"e_1_2_1_35_1","volume-title":"Proceedings of the 5th ACM International Conference on Multimedia Retrieval (ICMR\u201915)","author":"Murthy Venkatesh N.","unstructured":"Venkatesh N. Murthy , Subhransu Maji , and R. Manmatha . 2015. Automatic image annotation using deep learning representations . In Proceedings of the 5th ACM International Conference on Multimedia Retrieval (ICMR\u201915) . ACM, 603\u2013606. Venkatesh N. Murthy, Subhransu Maji, and R. Manmatha. 2015. Automatic image annotation using deep learning representations. In Proceedings of the 5th ACM International Conference on Multimedia Retrieval (ICMR\u201915). ACM, 603\u2013606."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/354756.354805"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.222"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.04.042"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5256-5"},{"key":"e_1_2_1_40_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. Retrieved from https:\/\/arxiv.org\/abs\/1409.1556.  Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. Retrieved from https:\/\/arxiv.org\/abs\/1409.1556."},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2909860"},{"key":"e_1_2_1_42_1","volume-title":"Designing a symmetric classifier for image annotation using multi-layer sparse coding. Image Vis. Comput. 69 (Jan","author":"Tariq Amara","year":"2018","unstructured":"Amara Tariq and Hassan Foroosh . Jan. 2018. Designing a symmetric classifier for image annotation using multi-layer sparse coding. Image Vis. Comput. 69 (Jan . 2018 ), 33\u201343. Amara Tariq and Hassan Foroosh. Jan. 2018. Designing a symmetric classifier for image annotation using multi-layer sparse coding. Image Vis. Comput. 69 (Jan. 2018), 33\u201343."},{"key":"e_1_2_1_43_1","volume-title":"Automatic image annotation via label transfer in the semantic space. Pattern Recogn. 71 (Nov","author":"Uricchio Tiberio","year":"2017","unstructured":"Tiberio Uricchio , Lamberto Ballan , Lorenzo Seidenari , and Alberto Del Bimbo . Nov. 2017. Automatic image annotation via label transfer in the semantic space. Pattern Recogn. 71 (Nov . 2017 ), 144\u2013157. Tiberio Uricchio, Lamberto Ballan, Lorenzo Seidenari, and Alberto Del Bimbo. Nov. 2017. Automatic image annotation via label transfer in the semantic space. Pattern Recogn. 71 (Nov. 2017), 144\u2013157."},{"key":"e_1_2_1_44_1","volume-title":"Diverse image annotation with missing labels. Pattern Recogn. 93 (Sept","author":"Verma Yashaswi","year":"2019","unstructured":"Yashaswi Verma . Sept. 2019. Diverse image annotation with missing labels. Pattern Recogn. 93 (Sept . 2019 ), 470\u2013484. Yashaswi Verma. Sept. 2019. Diverse image annotation with missing labels. Pattern Recogn. 93 (Sept. 2019), 470\u2013484."},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-016-0927-0"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/985692.985733"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2015.2497270"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304889.3305061"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098141"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2011.05.013"},{"key":"e_1_2_1_51_1","volume-title":"Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201910)","author":"Zhang Shaoting","unstructured":"Shaoting Zhang , Junzhou Huang , Yuchi Huang , Yang Yu , Hongsheng Li , and Dimitris N. Metaxas . 2010. Automatic image annotation using group sparsity . In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201910) . IEEE, 3312\u20133319. Shaoting Zhang, Junzhou Huang, Yuchi Huang, Yang Yu, Hongsheng Li, and Dimitris N. Metaxas. 2010. Automatic image annotation using group sparsity. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201910). IEEE, 3312\u20133319."},{"key":"e_1_2_1_52_1","volume-title":"Automatic image annotation via compact graph based semi-supervised learning. Knowl.-Based Syst. 76 (Mar","author":"Zhao Mingbo","year":"2015","unstructured":"Mingbo Zhao , Tommy W. S. Chow , Zhao Zhang , and Bing Li. Mar . 2015. Automatic image annotation via compact graph based semi-supervised learning. Knowl.-Based Syst. 76 (Mar . 2015 ), 148\u2013165. Mingbo Zhao, Tommy W. S. Chow, Zhao Zhang, and Bing Li. Mar. 2015. Automatic image annotation via compact graph based semi-supervised learning. Knowl.-Based Syst. 76 (Mar. 2015), 148\u2013165."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-019-08568-z"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3426974","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3426974","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:24Z","timestamp":1750197744000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3426974"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,28]]},"references-count":53,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,2,28]]}},"alternative-id":["10.1145\/3426974"],"URL":"https:\/\/doi.org\/10.1145\/3426974","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,28]]},"assertion":[{"value":"2020-02-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-09-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-04-16","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}