{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T15:30:10Z","timestamp":1759159810516,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T00:00:00Z","timestamp":1606262400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61772281"],"award-info":[{"award-number":["61772281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61703212"],"award-info":[{"award-number":["61703212"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,8]]},"DOI":"10.1007\/s11042-020-10171-6","type":"journal-article","created":{"date-parts":[[2020,11,25]],"date-time":"2020-11-25T01:02:37Z","timestamp":1606266157000},"page":"30761-30773","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Identifying forged seal imprints using positive and unlabeled learning"],"prefix":"10.1007","volume":"80","author":[{"given":"Leiming","family":"Yan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shikun","family":"Tong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,25]]},"reference":[{"issue":"2","key":"10171_CR1","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s10844-019-00549-w","volume":"53","author":"T Basile","year":"2019","unstructured":"Basile T, Mauro N, Esposito F, Ferilli S, Vergari A (2019) Ensembles of density estimators for positive-unlabeled learning. J Intell Inf Syst 53(2):199\u2013217. https:\/\/doi.org\/10.1007\/s10844-019-00549-w","journal-title":"J Intell Inf Syst"},{"key":"10171_CR2","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s10994-020-05877-5","volume":"109","author":"J Bekker","year":"2018","unstructured":"Bekker J, Davis J (2018) Learning from positive and unlabeled data: a survey. Mach Learn 109:719\u2013760","journal-title":"Mach Learn"},{"issue":"11","key":"10171_CR3","doi-asserted-by":"publisher","first-page":"1807","DOI":"10.1016\/0031-3203(96)00032-5","volume":"29","author":"YS Chen","year":"1996","unstructured":"Chen YS (1996) Automatic identification for a Chinese seal image. Pattern Recogn 29(11):1807\u20131820. https:\/\/doi.org\/10.1016\/0031-3203(96)00032-5","journal-title":"Pattern Recogn"},{"issue":"1","key":"10171_CR4","doi-asserted-by":"publisher","first-page":"239","DOI":"10.32604\/cmc.2019.03572","volume":"59","author":"X Chen","year":"2019","unstructured":"Chen X, Zhong H, Bao Z (2019) A GLCM-feature-based approach for reversible image transformation. Comp Mater Cont 59(1):239\u2013255. https:\/\/doi.org\/10.32604\/cmc.2019.03572","journal-title":"Comp Mater Cont"},{"key":"10171_CR5","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.neucom.2014.10.081","volume":"160","author":"M Claesen","year":"2015","unstructured":"Claesen M, Smet FD, Suykens J, Moor BD (2015) A robust ensemble approach to learn from positive and unlabeled data using SVM base models. Neurocomputing 160:73\u201384. https:\/\/doi.org\/10.1016\/j.neucom.2014.10.081","journal-title":"Neurocomputing"},{"key":"10171_CR6","doi-asserted-by":"publisher","unstructured":"Elkan, Noto K (2018) Learning classifiers from only positive and unlabeled data. In: Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-2008). https:\/\/doi.org\/10.1145\/1401890.1401920","DOI":"10.1145\/1401890.1401920"},{"key":"10171_CR7","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.patrec.2017.03.007","volume":"90","author":"H Gan","year":"2019","unstructured":"Gan H, Zhang Y, Song Q (2019) Bayesian belief network for positive unlabeled learning with uncertainty. Pattern Recogn Lett 90:28\u201335. https:\/\/doi.org\/10.1016\/j.patrec.2017.03.007","journal-title":"Pattern Recogn Lett"},{"key":"10171_CR8","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1109\/TNNLS.2019.2892403","volume":"30","author":"C Gong","year":"2019","unstructured":"Gong C, Liu T, Yang J, Tao D (2019) Large-margin label-calibrated support vector Machines for Positive and Unlabeled Learning. IEEE Transact Neur Netw Learn Syst 30:1396\u20131409. https:\/\/doi.org\/10.1109\/TNNLS.2019.2892403","journal-title":"IEEE Transact Neur Netw Learn Syst"},{"key":"10171_CR9","doi-asserted-by":"publisher","first-page":"3471","DOI":"10.1109\/TCSVT.2019.2903563","volume":"30","author":"C Gong","year":"2019","unstructured":"Gong C, Shi H, Yang J, Yang J (2019) Multi-manifold positive and unlabeled learning for visual analysis. IEEE Transact Circ Syst Vid Technol 30:3471\u20133483. https:\/\/doi.org\/10.1109\/TCSVT.2019.2903563","journal-title":"IEEE Transact Circ Syst Vid Technol"},{"key":"10171_CR10","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1016\/j.ins.2020.03.021","volume":"523","author":"H Ju","year":"2020","unstructured":"Ju H, Lee D, Hwang J, Namkung J, Yu H (2020) PUMAD: PU metric learning for anomaly detection. Inf Sci 523:167\u2013183. https:\/\/doi.org\/10.1016\/j.ins.2020.03.021","journal-title":"Inf Sci"},{"issue":"20","key":"10171_CR11","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1007\/s10994-019-05836-9","volume":"109","author":"Y Kwon","year":"2020","unstructured":"Kwon Y, Kim W, Sugiyama M, Paik MC (2020) Principled analytic classifier for positive-unlabeled learning via weighted integral probability metric. Mach Learn 109(20):513\u2013532. https:\/\/doi.org\/10.1007\/s10994-019-05836-9","journal-title":"Mach Learn"},{"key":"10171_CR12","unstructured":"Lee WS, Liu B (2003) Learning with positive and unlabeled samples using weighted logistic regression. In: ICML, vol 3, pp. 448\u2013455"},{"key":"10171_CR13","doi-asserted-by":"publisher","unstructured":"Li D (2010) Color seal segmentation and identification. Technological Developments in Networking, Education and Automation, pp. 333\u2013334. https:\/\/doi.org\/10.1007\/978-90-481-9151-2_58","DOI":"10.1007\/978-90-481-9151-2_58"},{"key":"10171_CR14","doi-asserted-by":"publisher","unstructured":"Li C , Hua X L (2019) Towards positive unlabeled learning for parallel data mining: a random Forest framework. In: The 10th International Conference on Advanced Data Mining and Applications, pp 573-587. https:\/\/doi.org\/10.1007\/978-3-319-14717-8_45","DOI":"10.1007\/978-3-319-14717-8_45"},{"key":"10171_CR15","doi-asserted-by":"publisher","unstructured":"Liu B, Dai Y, Li X et al (2003) Building text classifiers using positive and unlabeled samples. In: proceedings of the 3rd IEEE international conference on data mining, pp 179\u2013188. https:\/\/doi.org\/10.1109\/ICDM.2003.1250918","DOI":"10.1109\/ICDM.2003.1250918"},{"key":"10171_CR16","doi-asserted-by":"publisher","unstructured":"Run L, Zhi F, Sheng W, Shou Y. (2007) Feature extraction of seal imprint based on the double-density dual-tree DWT. Lect Notes Comp Sci 4488. https:\/\/doi.org\/10.1007\/978-3-540-72586-2_146","DOI":"10.1007\/978-3-540-72586-2_146"},{"key":"10171_CR17","unstructured":"Sakai T, Plessis M, Niu G, Sugiyama M (2017) Semi-supervised classification based on classification from positive and unlabeled data. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp 2998-3006"},{"key":"10171_CR18","doi-asserted-by":"publisher","unstructured":"Su YC, Ueng YL, Chung WH (2019) SVM-based seal imprint verification using edge difference. IEEE Access PP(99):1-1. https:\/\/doi.org\/10.1109\/ACCESS.2019.2945045","DOI":"10.1109\/ACCESS.2019.2945045"},{"key":"10171_CR19","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1007\/978-3-642-19376-7_5","volume":"6540","author":"X Wang","year":"2011","unstructured":"Wang X, Chen Y (2011) A novel seal imprint verification method based on analysis of difference images and symbolic representation. Lect Notes Comput Sci 6540:56\u201367. https:\/\/doi.org\/10.1007\/978-3-642-19376-7_5","journal-title":"Lect Notes Comput Sci"},{"issue":"3","key":"10171_CR20","doi-asserted-by":"publisher","first-page":"1827016","DOI":"10.1155\/2017\/1827016","volume":"2017","author":"Q Wang","year":"2017","unstructured":"Wang Q, Luo Z, Huang J, Feng Y (2017) Liu Z (2017) a novel ensemble method for imbalanced data learning: bagging of extrapolation-SMOTE SVM. Comput Intell Neurosci 2017(3):1827016\u20131827011. https:\/\/doi.org\/10.1155\/2017\/1827016","journal-title":"Comput Intell Neurosci"},{"issue":"1","key":"10171_CR21","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger KQ, Saul LK (2009) Distance metric learning for large margin nearest neighbor classification. J Mach Learn Res 10(1):207\u2013244","journal-title":"J Mach Learn Res"},{"issue":"1","key":"10171_CR22","doi-asserted-by":"publisher","first-page":"91","DOI":"10.3970\/cmc.2018.02771","volume":"56","author":"Q Wu","year":"2018","unstructured":"Wu Q, Li Y, Lin Y, Zhou R (2018) Weighted sparse image classification based on low rank representation. Comp Mater Cont 56(1):91\u2013105. https:\/\/doi.org\/10.3970\/cmc.2018.02771","journal-title":"Comp Mater Cont"},{"key":"10171_CR23","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.1109\/TCYB.2018.2877161","volume":"50","author":"Z Wu","year":"2018","unstructured":"Wu Z, Cao J, Wang Y, Wang Y, Zhang L, Wu J (2018) hPSD: a hybrid PU-learning-based spammer detection model for product reviews. IEEE Transact Cybernet 50:1595\u20131606. https:\/\/doi.org\/10.1109\/TCYB.2018.2877161","journal-title":"IEEE Transact Cybernet"},{"key":"10171_CR24","doi-asserted-by":"publisher","first-page":"78500D","DOI":"10.1117\/12.870202","volume":"7850","author":"H Zhang","year":"2010","unstructured":"Zhang H, He J (2010) Automatic seal imprint verification by quantifying edge difference. Optoelectron Imaging Multimed Technol 7850:78500D. https:\/\/doi.org\/10.1117\/12.870202","journal-title":"Optoelectron Imaging Multimed Technol"},{"issue":"3","key":"10171_CR25","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1145\/2602186","volume":"56","author":"L Zheng","year":"2018","unstructured":"Zheng L, Song C (2018) Fast near-duplicate image detection in Riemannian space by a novel hashing scheme. Comp Mater Cont 56(3):529\u2013539. https:\/\/doi.org\/10.1145\/2602186","journal-title":"Comp Mater Cont"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10171-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-10171-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-10171-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,15]],"date-time":"2021-09-15T05:36:43Z","timestamp":1631684203000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-10171-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,25]]},"references-count":25,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["10171"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-10171-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2020,11,25]]},"assertion":[{"value":"29 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}