{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T23:02:24Z","timestamp":1778626944680,"version":"3.51.4"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T00:00:00Z","timestamp":1600473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T00:00:00Z","timestamp":1600473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Key R&D Program of China","award":["2017YFB0306400"],"award-info":[{"award-number":["2017YFB0306400"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61773104"],"award-info":[{"award-number":["61773104"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2021,4]]},"DOI":"10.1007\/s11760-020-01778-1","type":"journal-article","created":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T12:02:48Z","timestamp":1600516968000},"page":"571-578","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Online learning method based on support vector machine for metallographic image segmentation"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7780-3213","authenticated-orcid":false,"given":"Mingchun","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0787-825X","authenticated-orcid":false,"given":"Dali","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shixin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinghao","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,9,19]]},"reference":[{"key":"1778_CR1","volume-title":"Materials Science and Engineering: An Introduction","author":"WD Callister","year":"2018","unstructured":"Callister, W.D., Rethwisch, D.G.: Materials Science and Engineering: An Introduction. Wiley, New York (2018)"},{"key":"1778_CR2","unstructured":"Chen, L., Jiang, M., Chen, J.: Image segmentation using iterative watersheding plus ridge detection. In: ICIP (2009)"},{"issue":"1","key":"1778_CR3","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.msea.2004.04.002","volume":"381","author":"WB Lievers","year":"2004","unstructured":"Lievers, W.B., Pilkey, A.K.: An evaluation of global thresholding techniques for the automatic image segmentation of automotive aluminum sheet alloys. Mater. Sci. Eng., A 381(1), 134\u2013142 (2004)","journal-title":"Mater. Sci. Eng., A"},{"key":"1778_CR4","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1016\/j.matdes.2017.12.049","volume":"141","author":"AI Campbell","year":"2018","unstructured":"Campbell, A.I., Murray, P., Yakushina, E., Marshall, S., Ion, W.: New methods for automatic quantification of microstructural features using digital image processing. Mater. Design 141, 395\u2013406 (2018)","journal-title":"Mater. Design"},{"issue":"2","key":"1778_CR5","doi-asserted-by":"publisher","first-page":"025001","DOI":"10.1088\/2051-672X\/aab73b","volume":"6","author":"X Zhenying","year":"2018","unstructured":"Zhenying, X., Jiandong, Z., Qi, Z., Yamba, P.: Algorithm based on regional separation for automatic grain boundary extraction using improved mean shift method. Surf. Topogr. Metrol. Prop. 6(2), 025001 (2018)","journal-title":"Surf. Topogr. Metrol. Prop."},{"issue":"1","key":"1778_CR6","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/S0924-0136(01)01057-3","volume":"117","author":"S Journaux","year":"2001","unstructured":"Journaux, S., Gouton, P., Paindavoine, M., Thauvin, G.: Evaluating creep in metals by grain boundary extraction using directional wavelets and mathematical morphology. J. Mater. Process. Technol. 117(1), 132\u2013145 (2001)","journal-title":"J. Mater. Process. Technol."},{"issue":"3","key":"1778_CR7","first-page":"033035","volume":"28","author":"S Zhang","year":"2019","unstructured":"Zhang, S., Chen, D., Liu, S., Zhang, P., Zhao, W.: Aluminum alloy microstructural segmentation method based on simple noniterative clustering and adaptive density-based spatial clustering of applications with noise. J. Electron. Imaging 28(3), 033035 (2019)","journal-title":"J. Electron. Imaging"},{"issue":"2","key":"1778_CR8","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.eswa.2012.07.062","volume":"40","author":"JP Papa","year":"2013","unstructured":"Papa, J.P., Nakamura, R.Y., De Albuquerque, V.H.C., Falc\u00e3o, A.X., Tavares, J.M.R.: Computer techniques towards the automatic characterization of graphite particles in metallographic images of industrial materials. Expert Syst. Appl. 40(2), 590\u2013597 (2013)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"1778_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-20438-6","volume":"8","author":"DS Bulgarevich","year":"2018","unstructured":"Bulgarevich, D.S., Tsukamoto, S., Kasuya, T., Demura, M., Watanabe, M.: Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures. Sci. Rep. 8(1), 1\u20138 (2018)","journal-title":"Sci. Rep."},{"key":"1778_CR10","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.commatsci.2015.08.011","volume":"110","author":"BL DeCost","year":"2015","unstructured":"DeCost, B.L., Holm, E.A.: A computer vision approach for automated analysis and classification of microstructural image data. Comput. Mater. Sci. 110, 126\u2013133 (2015)","journal-title":"Comput. Mater. Sci."},{"key":"1778_CR11","doi-asserted-by":"publisher","first-page":"324","DOI":"10.1016\/j.commatsci.2018.03.004","volume":"148","author":"J Gola","year":"2018","unstructured":"Gola, J., Britz, D., Staudt, T., Winter, M., Schneider, A.S., Ludovici, M., M\u00fccklich, F.: Advanced microstructure classification by data mining methods. Comput. Mater. Sci. 148, 324\u2013335 (2018)","journal-title":"Comput. Mater. Sci."},{"issue":"7","key":"1778_CR12","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1016\/j.ndteint.2009.05.002","volume":"42","author":"VHC de Albuquerque","year":"2009","unstructured":"de Albuquerque, V.H.C., de Alexandria, A.R., Cortez, P.C., Tavares, J.M.R.: Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images. NDT & E Int. 42(7), 644\u2013651 (2009)","journal-title":"NDT & E Int."},{"issue":"1","key":"1778_CR13","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1002\/jemt.20870","volume":"74","author":"VHC de Albuquerque","year":"2011","unstructured":"de Albuquerque, V.H.C., Silva, C.C., Menezes, T.I.D.S., Farias, J.P., Tavares, J.M.R.: Automatic evaluation of nickel alloy secondary phases from SEM images. Microsc. Res. Tech. 74(1), 36\u201346 (2011)","journal-title":"Microsc. Res. Tech."},{"key":"1778_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"1778_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-20037-5","volume":"8","author":"SM Azimi","year":"2018","unstructured":"Azimi, S.M., Britz, D., Engstler, M., Fritz, M., M\u00fccklich, F.: Advanced steel microstructural classification by deep learning methods. Sci. Rep. 8(1), 1\u201314 (2018)","journal-title":"Sci. Rep."},{"issue":"5","key":"1778_CR16","first-page":"053018","volume":"28","author":"D Chen","year":"2019","unstructured":"Chen, D., Zhang, P., Liu, S., Chen, Y., Zhao, W.: Aluminum alloy microstructural segmentation in micrograph with hierarchical parameter transfer learning method. J. Electron. Imaging 28(5), 053018 (2019)","journal-title":"J. Electron. Imaging"},{"key":"1778_CR17","doi-asserted-by":"publisher","first-page":"107857","DOI":"10.1016\/j.measurement.2020.107857","volume":"162","author":"M Li","year":"2020","unstructured":"Li, M., Chen, D., Liu, S., Liu, F.: Grain boundary detection and second phase segmentation based on multi-task learning and generative adversarial network. Measurement 162, 107857 (2020)","journal-title":"Measurement"},{"key":"1778_CR18","first-page":"165","volume":"13","author":"O Dekel","year":"2012","unstructured":"Dekel, O., Gilad-Bachrach, R., Shamir, O., Xiao, L.: Optimal distributed online prediction using mini-batches. J. Mach. Learn. Res 13, 165\u2013202 (2012)","journal-title":"J. Mach. Learn. Res"},{"issue":"3","key":"1778_CR19","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1137\/S0097539703432542","volume":"34","author":"N Cesa-Bianchi","year":"2005","unstructured":"Cesa-Bianchi, N., Conconi, A., Gentile, C.: A second-order perceptron algorithm. SIAM J. Comput. 34(3), 640\u2013668 (2005)","journal-title":"SIAM J. Comput."},{"issue":"1","key":"1778_CR20","doi-asserted-by":"publisher","first-page":"121","DOI":"10.4086\/toc.2012.v008a006","volume":"8","author":"S Arora","year":"2012","unstructured":"Arora, S., Hazan, E., Kale, S.: The multiplicative weights update method: a meta-algorithm and applications. Theory Comput. 8(1), 121\u2013164 (2012)","journal-title":"Theory Comput."},{"key":"1778_CR21","unstructured":"McMahan, H. B.: Follow-the-regularized-leader and mirror descent: equivalence theorems and L1 regularization. In: AISTATS (2011)"},{"issue":"4","key":"1778_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3156684","volume":"9","author":"J Lu","year":"2018","unstructured":"Lu, J., Sahoo, D., Zhao, P., Hoi, S.C.: Sparse passive-aggressive learning for bounded online kernel methods. ACM Trans. Intell. Syst. Technol. 9(4), 1\u201327 (2018)","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"4","key":"1778_CR23","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s11760-015-0753-9","volume":"9","author":"\u0130 \u00dclk\u00fc","year":"2015","unstructured":"\u00dclk\u00fc, \u0130., T\u00f6reyin, B.U.: Sparse coding of hyperspectral imagery using online learning. Signal Image Video Process. 9(4), 959\u2013966 (2015)","journal-title":"Signal Image Video Process."},{"key":"1778_CR24","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: MICCAI (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"3","key":"1778_CR25","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","volume":"36","author":"P Comon","year":"1994","unstructured":"Comon, P.: Independent component analysis, a new concept? Signal Process. 36(3), 287\u2013314 (1994)","journal-title":"Signal Process."},{"issue":"1","key":"1778_CR26","first-page":"3117","volume":"18","author":"HB McMahan","year":"2017","unstructured":"McMahan, H.B.: A survey of algorithms and analysis for adaptive online learning. J. Mach. Learn. Res 18(1), 3117\u20133166 (2017)","journal-title":"J. Mach. Learn. Res"},{"key":"1778_CR27","first-page":"2643","volume":"10","author":"F Orabona","year":"2009","unstructured":"Orabona, F., Keshet, J., Caputo, B.: Bounded kernel-based online learning. J. Mach. Learn. Res 10, 2643\u20132666 (2009)","journal-title":"J. Mach. Learn. Res"},{"key":"1778_CR28","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected crfs with gaussian edge potentials. In: NIPS (2011)"},{"key":"1778_CR29","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1023\/A:1022602019183","volume":"3","author":"DE Goldberg","year":"1988","unstructured":"Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3, 95\u201399 (1988)","journal-title":"Mach. Learn."},{"key":"1778_CR30","doi-asserted-by":"publisher","DOI":"10.1093\/oso\/9780195099713.001.0001","volume-title":"Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms","author":"T Back","year":"1996","unstructured":"Back, T.: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, Oxford (1996)"},{"issue":"2","key":"1778_CR31","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1561\/2200000018","volume":"4","author":"S Shalev-Shwartz","year":"2012","unstructured":"Shalev-Shwartz, S.: Online learning and online convex optimization. Found. Trends Mach. Learn. 4(2), 107\u2013194 (2012)","journal-title":"Found. Trends Mach. Learn."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-020-01778-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-020-01778-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-020-01778-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,18]],"date-time":"2021-09-18T23:53:14Z","timestamp":1632009194000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-020-01778-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,19]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,4]]}},"alternative-id":["1778"],"URL":"https:\/\/doi.org\/10.1007\/s11760-020-01778-1","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,19]]},"assertion":[{"value":"27 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 September 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}