{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:55:00Z","timestamp":1740099300541,"version":"3.37.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030205171"},{"type":"electronic","value":"9783030205188"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-20518-8_22","type":"book-chapter","created":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T19:02:40Z","timestamp":1559674960000},"page":"258-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Ensemble Transfer Learning Framework for Vessel Size Estimation from 2D Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8877-4689","authenticated-orcid":false,"given":"Mario","family":"Mili\u010devi\u0107","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7786-454X","authenticated-orcid":false,"given":"Krunoslav","family":"\u017dubrini\u0107","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9642-7732","authenticated-orcid":false,"given":"Ivan","family":"Grbavac","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8330-029X","authenticated-orcid":false,"given":"Ana","family":"Ke\u0161elj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"22_CR1","doi-asserted-by":"publisher","DOI":"10.4324\/9781315665061","volume-title":"Sensation and Perception","author":"H Foley","year":"2015","unstructured":"Foley, H., Matlin, M.: Sensation and Perception. Psychology Press, London (2015)"},{"key":"22_CR2","unstructured":"Yantis, S.: Sensation and Perception. Macmillan International Higher Education (2013)"},{"issue":"6694","key":"22_CR3","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1038\/29299","volume":"394","author":"GC DeAngelis","year":"1998","unstructured":"DeAngelis, G.C., Cumming, B.G., Newsome, W.T.: Cortical area MT and the perception of stereoscopic depth. Nature 394(6694), 677 (1998). \n                      https:\/\/doi.org\/10.1038\/29299","journal-title":"Nature"},{"key":"22_CR4","unstructured":"International Convention on Tonnage Measurement of Ships. \n                      http:\/\/www.imo.org\/en\/about\/conventions\/listofconventions\/pages\/international-convention-on-tonnage-measurement-of-ships.aspx\n                      \n                    . Accessed 4 Feb 2019"},{"key":"22_CR5","unstructured":"Standley, T., Sener, O., Chen, D., Savarese, S.: image2mass: estimating the mass of an object from its image. In: Conference on Robot Learning, pp. 324\u2013333 (2017)"},{"issue":"2","key":"22_CR6","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.compag.2011.02.001","volume":"76","author":"S Tasdemir","year":"2011","unstructured":"Tasdemir, S., Urkmez, A., Inal, S.: Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Comput. Electron. Agric. 76(2), 189\u2013197 (2011). \n                      https:\/\/doi.org\/10.1016\/j.compag.2011.02.001","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"22_CR7","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1080\/09712119.2007.9706877","volume":"32","author":"Y Bozkurt","year":"2007","unstructured":"Bozkurt, Y., Aktan, S., Ozkaya, S.: Body weight prediction using digital image analysis for slaughtered beef cattle. J. Appl. Anim. Res. 32(2), 195\u2013198 (2007). \n                      https:\/\/doi.org\/10.1080\/09712119.2007.9706877","journal-title":"J. Appl. Anim. Res."},{"key":"22_CR8","series-title":"The International Federation for Information Processing","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1007\/978-0-387-77253-0_100","volume-title":"Computer And Computing Technologies In Agriculture, Volume II","author":"Y Yang","year":"2008","unstructured":"Yang, Y., Teng, G.: Estimating pig weight from 2D images. In: Li, D. (ed.) CCTA 2007. TIFIP, vol. 259, pp. 1471\u20131474. Springer, Boston (2008). \n                      https:\/\/doi.org\/10.1007\/978-0-387-77253-0_100"},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.compag.2018.03.003","volume":"148","author":"A Pezzuolo","year":"2018","unstructured":"Pezzuolo, A., Guarino, M., Sartori, L., Gonz\u00e1lez, L.A., Marinello, F.: On-barn pig weight estimation based on body measurements by a Kinect v1 depth camera. Comput. Electron. Agric. 148, 29\u201336 (2018). \n                      https:\/\/doi.org\/10.1016\/j.compag.2018.03.003","journal-title":"Comput. Electron. Agric."},{"issue":"1","key":"22_CR10","doi-asserted-by":"publisher","first-page":"51","DOI":"10.18178\/joig.4.1.51-54","volume":"4","author":"K Sabanci","year":"2016","unstructured":"Sabanci, K., Ekinci, S., Karahan, A.M., Aydin, C.: Weight estimation of wheat by using image processing techniques. J. Image Graph. 4(1), 51\u201354 (2016). \n                      https:\/\/doi.org\/10.18178\/joig.4.1.51-54","journal-title":"J. Image Graph."},{"key":"22_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/978-3-642-27172-4_50","volume-title":"Swarm, Evolutionary, and Memetic Computing","author":"P Javadikia","year":"2011","unstructured":"Javadikia, P., Dehrouyeh, M.H., Naderloo, L., Rabbani, H., Lorestani, A.N.: Measuring the weight of egg with image processing and ANFIS model. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds.) SEMCCO 2011. LNCS, vol. 7076, pp. 407\u2013416. Springer, Heidelberg (2011). \n                      https:\/\/doi.org\/10.1007\/978-3-642-27172-4_50"},{"key":"22_CR12","doi-asserted-by":"publisher","unstructured":"Wu, J., Lim, J.J., Zhang, H., Tenenbaum, J.B., Freeman, W.T.: Physics 101: learning physical object properties from unlabeled videos. In: BMVC, vol. 2 (2016). \n                      https:\/\/doi.org\/10.5244\/c.30.39","DOI":"10.5244\/c.30.39"},{"key":"22_CR13","doi-asserted-by":"publisher","unstructured":"He, Y., Xu, C., Khanna, N., Boushey, C.J., Delp, E.J.: Food image analysis: segmentation, identification and weight estimation. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136 (2013). \n                      https:\/\/doi.org\/10.1109\/icme.2013.6607548","DOI":"10.1109\/icme.2013.6607548"},{"key":"22_CR14","doi-asserted-by":"publisher","unstructured":"Chae, J., et al.: Volume estimation using food specific shape templates in mobile image-based dietary assessment. In: Computational Imaging IX, vol. 7873. International Society for Optics and Photonics (2011). \n                      https:\/\/doi.org\/10.1117\/12.876669","DOI":"10.1117\/12.876669"},{"key":"22_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1007\/978-3-319-46484-8_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"CB Choy","year":"2016","unstructured":"Choy, C.B., Xu, D., Gwak, J.Y., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3D object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628\u2013644. Springer, Cham (2016). \n                      https:\/\/doi.org\/10.1007\/978-3-319-46484-8_38"},{"key":"22_CR16","doi-asserted-by":"publisher","unstructured":"Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 605\u2013613 (2017). \n                      https:\/\/doi.org\/10.1109\/cvpr.2017.264","DOI":"10.1109\/cvpr.2017.264"},{"key":"22_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s41074-017-0033-4","volume":"9","author":"B Solmaz","year":"2017","unstructured":"Solmaz, B., Gundogdu, E., Yucesoy, V., Koc, A.: Generic and attribute-specific deep representations for maritime vessels. IPSJ T. Comput. Vis. Appl. 9, 1\u201318 (2017). \n                      https:\/\/doi.org\/10.1186\/s41074-017-0033-4","journal-title":"IPSJ T. Comput. Vis. Appl."},{"key":"22_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-319-54193-8_11","volume-title":"Computer Vision \u2013 ACCV 2016","author":"E Gundogdu","year":"2017","unstructured":"Gundogdu, E., Solmaz, B., Y\u00fccesoy, V., Ko\u00e7, A.: MARVEL: a large-scale image dataset for maritime vessels. In: Lai, S.-H., Lepetit, V., Nishino, K., Sato, Y. (eds.) ACCV 2016. LNCS, vol. 10115, pp. 165\u2013180. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-54193-8_11"},{"key":"22_CR19","unstructured":"Ship Photos and Ship Tracker. \n                      http:\/\/www.shipspotting.com\n                      \n                    . Accessed 10 Oct 2018"},{"issue":"3\/4","key":"22_CR20","doi-asserted-by":"publisher","first-page":"591","DOI":"10.2307\/2333709","volume":"52","author":"SS Shapiro","year":"1965","unstructured":"Shapiro, S.S., Wilk, M.B.: An analysis of variance test for normality (complete samples). Biometrika 52(3\/4), 591\u2013611 (1965). \n                      https:\/\/doi.org\/10.2307\/2333709","journal-title":"Biometrika"},{"key":"22_CR21","volume-title":"Deep Learning with Python","author":"F Chollet","year":"2017","unstructured":"Chollet, F.: Deep Learning with Python, 1st edn. Manning Publications Co., Greenwich (2017)","edition":"1"},{"key":"22_CR22","unstructured":"Abadi, M. et al.: TensorFlow: large-scale machine learning on heterogeneous systems. \n                      http:\/\/www.tensorflow.org\n                      \n                     (2015)"},{"key":"22_CR23","first-page":"460","volume":"13","author":"M Mili\u010devi\u0107","year":"2018","unstructured":"Mili\u010devi\u0107, M., \u017dubrini\u0107, K., Obradovi\u0107, I., Sjekavica, T.: Data augmentation and transfer learning for limited dataset ship classification. WSEAS Trans. Syst. Control 13, 460\u2013465 (2018)","journal-title":"WSEAS Trans. Syst. Control"},{"key":"22_CR24","unstructured":"Lathuili\u00e8re, S., Mesejo, P., Alameda-Pineda, X., Horaud, R.: A comprehensive analysis of deep regression. arXiv preprint \n                      arXiv:1803.08450\n                      \n                     (2018)"},{"key":"22_CR25","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint \n                      arXiv:1409.1556\n                      \n                     (2014)"},{"key":"22_CR26","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016). \n                      https:\/\/doi.org\/10.1109\/cvpr.2016.308","DOI":"10.1109\/cvpr.2016.308"},{"key":"22_CR27","doi-asserted-by":"publisher","unstructured":"Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251\u20131258 (2017). \n                      https:\/\/doi.org\/10.1109\/cvpr.2017.195","DOI":"10.1109\/cvpr.2017.195"},{"key":"22_CR28","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016). \n                      https:\/\/doi.org\/10.1109\/cvpr.2016.90","DOI":"10.1109\/cvpr.2016.90"},{"issue":"10","key":"22_CR29","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/tkde.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010). \n                      https:\/\/doi.org\/10.1109\/tkde.2009.191","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"22_CR30","unstructured":"Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647\u2013655 (2014)"},{"key":"22_CR31","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"22_CR32","unstructured":"Huh, M., Agrawal, P., Efros, A.A.: What makes ImageNet good for transfer learning? arXiv preprint \n                      arXiv:1608.08614\n                      \n                     (2016)"},{"key":"22_CR33","unstructured":"Drucker, H., Burges, C.J., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, pp. 155\u2013161 (1997)"},{"issue":"1","key":"22_CR34","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"22_CR35","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint \n                      arXiv:1502.03167\n                      \n                     (2015)"},{"issue":"1","key":"22_CR36","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"22_CR37","unstructured":"Masters, D., Luschi, C.: Revisiting small batch training for deep neural networks. arXiv preprint \n                      arXiv:1804.07612\n                      \n                     (2018)"},{"key":"22_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","volume-title":"Multiple Classifier Systems","author":"TG Dietterich","year":"2000","unstructured":"Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1\u201315. Springer, Heidelberg (2000). \n                      https:\/\/doi.org\/10.1007\/3-540-45014-9_1"},{"key":"22_CR39","unstructured":"Lee, S., Purushwalkam, S., Cogswell, M., Crandall, D., Batra, D.: Why M heads are better than one: training a diverse ensemble of deep networks. arXiv preprint \n                      arXiv:1511.06314\n                      \n                     (2015)"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-20518-8_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,4]],"date-time":"2019-06-04T19:12:30Z","timestamp":1559675550000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-20518-8_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030205171","9783030205188"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-20518-8_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"16 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Gran Canaria","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 June 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iwann.uma.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"210","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"150","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"71% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"2,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}