{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:28:14Z","timestamp":1750307294660,"version":"3.41.0"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2011,2,1]],"date-time":"2011-02-01T00:00:00Z","timestamp":1296518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["1R01-GM079688-01"],"award-info":[{"award-number":["1R01-GM079688-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["RG67\/07"],"award-info":[{"award-number":["RG67\/07"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000145","name":"Division of Information and Intelligent Systems","doi-asserted-by":"publisher","award":["IIS-0643494"],"award-info":[{"award-number":["IIS-0643494"]}],"id":[{"id":"10.13039\/100000145","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Intell. Syst. Technol."],"published-print":{"date-parts":[[2011,2]]},"abstract":"<jats:p>\n            Automated photo tagging is an important technique for many intelligent multimedia information systems, for example, smart photo management system and intelligent digital media library. To attack the challenge, several machine learning techniques have been developed and applied for automated photo tagging. For example, supervised learning techniques have been applied to automated photo tagging by training statistical classifiers from a collection of manually labeled examples. Although the existing approaches work well for small testbeds with relatively small number of annotation words, due to the long-standing challenge of object recognition, they often perform poorly in large-scale problems. Another limitation of the existing approaches is that they require a set of high-quality labeled data, which is not only expensive to collect but also time consuming. In this article, we investigate a social image based annotation scheme by exploiting\n            <jats:italic>implicit<\/jats:italic>\n            side information that is available for a large number of social photos from the social web sites. The key challenge of our intelligent annotation scheme is how to learn an effective distance metric based on\n            <jats:italic>implicit<\/jats:italic>\n            side information (visual or textual) of social photos. To this end, we present a novel \u201cProbabilistic Distance Metric Learning\u201d (PDML) framework, which can learn optimized metrics by effectively exploiting the\n            <jats:italic>implicit<\/jats:italic>\n            side information vastly available on the social web. We apply the proposed technique to photo annotation tasks based on a large social image testbed with over 1 million tagged photos crawled from a social photo sharing portal. Encouraging results show that the proposed technique is effective and promising for social photo based annotation tasks.\n          <\/jats:p>","DOI":"10.1145\/1899412.1899417","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T20:56:02Z","timestamp":1350075362000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":12,"title":["Distance metric learning from uncertain side information for automated photo tagging"],"prefix":"10.1145","volume":"2","author":[{"given":"Lei","family":"Wu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Steven C.H.","family":"Hoi","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong","family":"Jin","sequence":"additional","affiliation":[{"name":"Michigan State University, East Lansing, MI"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianke","family":"Zhu","sequence":"additional","affiliation":[{"name":"Zhejiang University, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nenghai","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2011,2,24]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1327452.1327494"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/1046920.1088704"},{"volume-title":"Pattern Recognition with Fuzzy Objective Function Algorithms","author":"Bezdek J. C.","key":"e_1_2_1_3_1","unstructured":"Bezdek , J. C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms . Kluwer Academic Publishers , Norwell, MA . Bezdek, J. C. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, Norwell, MA."},{"key":"e_1_2_1_4_1","first-page":"351","article-title":"Convergence of alternating optimization","volume":"11","author":"Bezdek J. C.","year":"2003","unstructured":"Bezdek , J. C. and Hathaway , R. J. 2003 . Convergence of alternating optimization . Neural, Parall. Sci. Comput. 11 , 4, 351 -- 368 . Bezdek, J. C. and Hathaway, R. J. 2003. Convergence of alternating optimization. Neural, Parall. Sci. Comput. 11, 4, 351--368.","journal-title":"Neural, Parall. Sci. Comput."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.61"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.164"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1273496.1273523"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390191"},{"volume-title":"Proceedings of the 2nd European Conference on Computer Vision (ECCV'02)","author":"Duygulu P.","key":"e_1_2_1_9_1","unstructured":"Duygulu , P. , Barnard , K. , de Freitas , J. , and Forsyth , D. A . 2002. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary . In Proceedings of the 2nd European Conference on Computer Vision (ECCV'02) . 97--112. Duygulu, P., Barnard, K., de Freitas, J., and Forsyth, D. A. 2002. Object recognition as machine translation: Learning a lexicon for a fixed image vocabulary. In Proceedings of the 2nd European Conference on Computer Vision (ECCV'02). 97--112."},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/1027527.1027660"},{"volume-title":"Introduction to Statistical Pattern Recognition","author":"Fukunaga K.","key":"e_1_2_1_11_1","unstructured":"Fukunaga , K. 1990. Introduction to Statistical Pattern Recognition . Elsevier . Fukunaga, K. 1990. Introduction to Statistical Pattern Recognition. Elsevier."},{"volume-title":"Proceedings of the Advances in Neural Information Processing Systems.","author":"Globerson A.","key":"e_1_2_1_12_1","unstructured":"Globerson , A. and Roweis , S . 2005. Metric learning by collapsing classes . In Proceedings of the Advances in Neural Information Processing Systems. Globerson, A. and Roweis, S. 2005. Metric learning by collapsing classes. 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In Proceedings of the Conference on Advances in Neural Information Processing Systems. 625--632 . He, X. and Zemel, R. S. 2008. Learning hybrid models for image annotation with partially labeled data. 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Jin, R., Wang, S., and Zhou, Y. 2009. Regularized distance metric learning: Theory and algorithm. 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