{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:25Z","timestamp":1760597965907,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030501426"},{"type":"electronic","value":"9783030501433"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","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":[[2020]]},"DOI":"10.1007\/978-3-030-50143-3_21","type":"book-chapter","created":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T21:03:01Z","timestamp":1591390981000},"page":"269-280","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Learning Tversky Similarity"],"prefix":"10.1007","author":[{"given":"Javad","family":"Rahnama","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eyke","family":"H\u00fcllermeier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,5]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Abraham, N., Khan, N.: A novel focal Tversky loss function with improved attention U-net for lesion segmentation. In: Proceedings ISBI, IEEE International Symposium on Biomedical Imaging (2019)","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"21_CR2","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/978-3-642-31718-7_22","volume-title":"Advances in Computational Intelligence","author":"M Baioletti","year":"2012","unstructured":"Baioletti, M., Coletti, G., Petturiti, D.: Weighted attribute combinations based similarity measures. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012. CCIS, vol. 299, pp. 211\u2013220. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-31718-7_22"},{"issue":"2","key":"21_CR3","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltru\u0161aitis","year":"2018","unstructured":"Baltru\u0161aitis, T., Ahuja, C., Morency, L.P.: Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41(2), 423\u2013443 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/978-3-540-68860-0_17","volume-title":"Computational Intelligence: Research Frontiers","author":"B Bouchon-Meunier","year":"2008","unstructured":"Bouchon-Meunier, B., Rifqi, M., Lesot, M.-J.: Similarities in fuzzy data mining: from a cognitive view to real-world applications. In: Zurada, J.M., Yen, G.G., Wang, J. (eds.) WCCI 2008. LNCS, vol. 5050, pp. 349\u2013367. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-68860-0_17"},{"key":"21_CR5","first-page":"1109","volume":"11","author":"G Chechik","year":"2010","unstructured":"Chechik, G., Sharma, V., Shalit, U., Bengio, S.: Large scale online learning of image similarity through ranking. J. Mach. Learn. Res. 11, 1109\u20131135 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR6","first-page":"747","volume":"10","author":"Y Chen","year":"2009","unstructured":"Chen, Y., Garcia, E.K., Gupta, M.R., Rahimi, A., Cazzanti, L.: Similarity-based classification: concepts and algorithms. J. Mach. Learn. Res. 10, 747\u2013776 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., Le Cun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 539\u2013546 (2005)","DOI":"10.1109\/CVPR.2005.202"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Coletti, G., Bouchon-Meunier, B.: Fuzzy similarity measures and measurement theory. In: Proceedings FUZZ-IEEE, International Conference on Fuzzy Systems, New Orleans, LA, USA, pp. 1\u20137 (2019)","DOI":"10.1109\/FUZZ-IEEE.2019.8858793"},{"issue":"16","key":"21_CR9","doi-asserted-by":"publisher","first-page":"6827","DOI":"10.1007\/s00500-018-03724-3","volume":"23","author":"G Coletti","year":"2019","unstructured":"Coletti, G., Bouchon-Meunier, B.: A study of similarity measures through the paradigm of measurement theory: the classic case. Soft. Comput. 23(16), 6827\u20136845 (2019)","journal-title":"Soft. Comput."},{"key":"21_CR10","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/978-3-319-61581-3_33","volume-title":"Symbolic and Quantitative Approaches to Reasoning with Uncertainty","author":"G Coletti","year":"2017","unstructured":"Coletti, G., Petturiti, D., Vantaggi, B.: Fuzzy weighted attribute combinations based\u00a0similarity measures. In: Antonucci, A., Cholvy, L., Papini, O. (eds.) ECSQARU 2017. LNCS (LNAI), vol. 10369, pp. 364\u2013374. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-61581-3_33"},{"issue":"2","key":"21_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1348246.1348248","volume":"40","author":"R Datta","year":"2008","unstructured":"Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: ideas, influences, and trends of the new age. ACM Comput. Surv. 40(2), 1\u201360 (2008)","journal-title":"ACM Comput. Surv."},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Deselaers, T., Ferrari, V.: Visual and semantic similarity in ImageNet. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1777\u20131784. IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995474"},{"key":"21_CR13","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121\u20132159 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Farhadi, A., Endres, I., Hoiem, D., Forsyth, D.: Describing objects by their attributes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1778\u20131785. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206772"},{"key":"21_CR15","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.imavis.2019.01.001","volume":"82","author":"N Garcia","year":"2019","unstructured":"Garcia, N., Vogiatzis, G.: Learning non-metric visual similarity for image retrieval. Image Vis. Comput. 82, 18\u201325 (2019)","journal-title":"Image Vis. Comput."},{"key":"21_CR16","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)"},{"key":"21_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"21_CR18","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 1097\u20131105 (2012)"},{"key":"21_CR19","unstructured":"Kulis, B., et al.: Metric learning: a survey. Found. Trends\u00ae Mach. Learn. 5(4), 287\u2013364 (2013)"},{"issue":"4","key":"21_CR20","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1093\/llc\/fqy006","volume":"33","author":"S Lang","year":"2018","unstructured":"Lang, S., Ommer, B.: Attesting similarity: supporting the organization and study of art image collections with computer vision. Digit. Scholarsh. Humanit. 33(4), 845\u2013856 (2018)","journal-title":"Digit. Scholarsh. Humanit."},{"issue":"1","key":"21_CR21","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1504\/IJKESDP.2009.021985","volume":"1","author":"M Lesot","year":"2009","unstructured":"Lesot, M., Rifqi, M., Benhadda, H.: Similarity measures for binary and numerical data: a survey. Int. J. Knowl. Eng. Soft Data Paradigms 1(1), 63\u201384 (2009)","journal-title":"Int. J. Knowl. Eng. Soft Data Paradigms"},{"issue":"1","key":"21_CR22","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1016\/j.patcog.2006.04.045","volume":"40","author":"Y Liu","year":"2007","unstructured":"Liu, Y., Zhang, D., Lu, G., Ma, W.Y.: A survey of content-based image retrieval with high-level semantics. Pattern Recogn. 40(1), 262\u2013282 (2007)","journal-title":"Pattern Recogn."},{"issue":"1\u20132","key":"21_CR23","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s11263-013-0695-z","volume":"108","author":"G Patterson","year":"2014","unstructured":"Patterson, G., Xu, C., Su, H., Hays, J.: The sun attribute database: beyond categories for deeper scene understanding. Int. J. Comput. Vision 108(1\u20132), 59\u201381 (2014)","journal-title":"Int. J. Comput. Vision"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Pedersen, T., Patwardhan, S., Michelizzi, J.: Wordnet::Similarity - measuring the relatedness of concepts. In: Proceedings AAAI, vol. 4, pp. 25\u201329 (2004)","DOI":"10.3115\/1614025.1614037"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Qian, G., Sural, S., Gu, Y., Pramanik, S.: Similarity between Euclidean and cosine angle distance for nearest neighbor queries. In: ACM Symposium on Applied Computing, pp. 1232\u20131237 (2004)","DOI":"10.1145\/967900.968151"},{"key":"21_CR26","series-title":"International Centre for Mechanical Sciences","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/978-3-7091-2690-5_12","volume-title":"Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence","author":"MM Richter","year":"1995","unstructured":"Richter, M.M.: On the notion of similarity in case-based reasoning. In: Della Riccia, G., Kruse, R., Viertl, R. (eds.) Proceedings of the ISSEK94 Workshop on Mathematical and Statistical Methods in Artificial Intelligence. ICMS, vol. 363, pp. 171\u2013183. Springer, Vienna (1995). https:\/\/doi.org\/10.1007\/978-3-7091-2690-5_12"},{"key":"21_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-319-67389-9_44","volume-title":"Machine Learning in Medical Imaging","author":"SSM Salehi","year":"2017","unstructured":"Salehi, S.S.M., Erdogmus, D., Gholipour, A.: Tversky loss function for image segmentation using 3D fully convolutional deep networks. In: Wang, Q., Shi, Y., Suk, H.-I., Suzuki, K. (eds.) MLMI 2017. LNCS, vol. 10541, pp. 379\u2013387. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67389-9_44"},{"issue":"9","key":"21_CR28","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/34.790428","volume":"21","author":"S Santini","year":"1999","unstructured":"Santini, S., Jain, R.: Similarity measures. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 871\u2013883 (1999)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"21_CR29","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1109\/34.895972","volume":"22","author":"AW Smeulders","year":"2000","unstructured":"Smeulders, A.W., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1349\u20131380 (2000)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR30","unstructured":"Strehl, A., Ghosh, J., Mooney, R.: Impact of similarity measures on web-page clustering. In: Workshop on Artificial Intelligence for Web Search (AAAI), vol. 58, p. 64 (2000)"},{"key":"21_CR31","unstructured":"Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: International Conference on Machine Learning, pp. 1139\u20131147 (2013)"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"21_CR33","doi-asserted-by":"crossref","unstructured":"Tang, H., Maitre, H., Boujemaa, N.: Similarity measures for satellite images with heterogeneous contents. In: Proceedings Urban Remote Sensing, pp. 1\u20139. IEEE (2007)","DOI":"10.1109\/URS.2007.371796"},{"issue":"2","key":"21_CR34","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/S0165-0114(99)00114-1","volume":"120","author":"YA Tolias","year":"2001","unstructured":"Tolias, Y.A., Panas, S.M., Tsoukalas, L.H.: Generalized fuzzy indices for similarity matching. Fuzzy Sets Syst. 120(2), 255\u2013270 (2001)","journal-title":"Fuzzy Sets Syst."},{"issue":"4","key":"21_CR35","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1037\/0033-295X.84.4.327","volume":"84","author":"A Tversky","year":"1977","unstructured":"Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327\u2013352 (1977)","journal-title":"Psychol. Rev."},{"key":"21_CR36","unstructured":"Tversky, A., Gati, I.: Similarity, separability, and the triangle inequality. Psychol. Rev. 89(2), (1982)"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Wang, J., et al.: Learning fine-grained image similarity with deep ranking. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1386\u20131393 (2014)","DOI":"10.1109\/CVPR.2014.180"},{"key":"21_CR38","first-page":"207","volume":"10","author":"KQ Weinberger","year":"2009","unstructured":"Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207\u2013244 (2009)","journal-title":"J. Mach. Learn. Res."},{"key":"21_CR39","unstructured":"Yue-Hei Ng, J., Yang, F., Davis, L.S.: Exploiting local features from deep networks for image retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPR), pp. 53\u201361 (2015)"}],"container-title":["Communications in Computer and Information Science","Information Processing and Management of Uncertainty in Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50143-3_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T01:26:49Z","timestamp":1722994009000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-50143-3_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030501426","9783030501433"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50143-3_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"5 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IPMU","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ipmu2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ipmu2020.inesc-id.pt\/","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 (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"213","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"146","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"69% - 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 (provided by the conference organizers)"}},{"value":"3,2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The IPMU 2020 was held virtually due to the coronavirus pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}