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J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2022,3,30]]},"abstract":"<jats:p> Obtaining discriminative features is one of the key problems in three-dimensional (3D) shape retrieval. Recently, deep metric learning-based 3D shape retrieval methods have attracted the researchers\u2019 attention and have achieved better performance. The triplet-center loss can learn more discriminative features than traditional classification loss, and it has been successfully used in deep metric learning-based 3D shape retrieval task. However, it has a hard margin parameter that only leverages part of the training data in each mini-batch. Moreover, the margin parameter is often determined by experience and remains unchanged during the training process. To overcome the above limitations, we propose the soft margin triplet-center loss, which replaces the margin with the nonparametric soft margin. Furthermore, we combined the proposed soft margin triplet-center loss with the softmax loss to improve the training efficiency and the retrieval performance. Extensive experimental results on two popular 3D shape retrieval datasets have validated the effectiveness of the soft margin triplet-center loss, and our proposed 3D shape retrieval method has achieved better performance than other state-of-the-art method. <\/jats:p>","DOI":"10.1142\/s0218001422500173","type":"journal-article","created":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T03:42:07Z","timestamp":1648438927000},"source":"Crossref","is-referenced-by-count":0,"title":["Soft Margin Triplet-Center Loss for Multi-View 3D Shape Retrieval"],"prefix":"10.1142","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1442-7166","authenticated-orcid":false,"given":"Ruting","family":"Cheng","sequence":"first","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.\u00a0R.\u00a0China"}]},{"given":"Fuzhou","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.\u00a0R.\u00a0China"}]},{"given":"Tianmeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.\u00a0R.\u00a0China"}]},{"given":"Hongmin","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.\u00a0R.\u00a0China"}]},{"given":"Hui","family":"Zeng","sequence":"additional","affiliation":[{"name":"Beijing Engineering Research Center of Industrial Spectrum Imaging, School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, P.\u00a0R.\u00a0China"},{"name":"Shunde Graduate School of University of Science and Technology Beijing, Foshan 528399, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2022,3,25]]},"reference":[{"key":"S0218001422500173BIB001","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.543"},{"key":"S0218001422500173BIB004","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"S0218001422500173BIB005","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00035"},{"key":"S0218001422500173BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2020.02.017"},{"key":"S0218001422500173BIB007","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3005434"},{"key":"S0218001422500173BIB008","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2016.2609814"},{"key":"S0218001422500173BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00208"},{"key":"S0218001422500173BIB010","doi-asserted-by":"publisher","DOI":"10.1145\/3042064"},{"key":"S0218001422500173BIB011","doi-asserted-by":"publisher","DOI":"10.1145\/3130348.3130374"},{"key":"S0218001422500173BIB012","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.702"},{"key":"S0218001422500173BIB013","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.99"},{"key":"S0218001422500173BIB014","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2395961"},{"key":"S0218001422500173BIB015","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2875512"},{"issue":"11","key":"S0218001422500173BIB016","first-page":"2579","volume":"9","author":"Maaten L. 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