{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T05:58:07Z","timestamp":1767851887495,"version":"3.49.0"},"reference-count":41,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2010,8,1]],"date-time":"2010-08-01T00:00:00Z","timestamp":1280620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2010,8]]},"abstract":"<jats:p>\n            Learning a good distance metric plays a vital role in many multimedia retrieval and data mining tasks. For example, a typical content-based image retrieval (CBIR) system often relies on an effective distance metric to measure similarity between any two images. Conventional CBIR systems simply adopting Euclidean distance metric often fail to return satisfactory results mainly due to the well-known semantic gap challenge. In this article, we present a novel framework of\n            <jats:italic>Semi-Supervised Distance Metric Learning<\/jats:italic>\n            for learning effective distance metrics by exploring the historical relevance feedback log data of a CBIR system and utilizing unlabeled data when log data are limited and noisy. We formally formulate the learning problem into a convex optimization task and then present a new technique, named as \u201cLaplacian Regularized Metric Learning\u201d (LRML). Two efficient algorithms are then proposed to solve the LRML task. Further, we apply the proposed technique to two applications. One direct application is for Collaborative Image Retrieval (CIR), which aims to explore the CBIR log data for improving the retrieval performance of CBIR systems. The other application is for Collaborative Image Clustering (CIC), which aims to explore the CBIR log data for enhancing the clustering performance of image pattern clustering tasks. We conduct extensive evaluation to compare the proposed LRML method with a number of competing methods, including 2 standard metrics, 3 unsupervised metrics, and 4 supervised metrics with side information. Encouraging results validate the effectiveness of the proposed technique.\n          <\/jats:p>","DOI":"10.1145\/1823746.1823752","type":"journal-article","created":{"date-parts":[[2010,8,31]],"date-time":"2010-08-31T13:05:55Z","timestamp":1283259955000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":118,"title":["Semi-supervised distance metric learning for collaborative image retrieval and clustering"],"prefix":"10.1145","volume":"6","author":[{"given":"Steven C.h.","family":"Hoi","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Columbia University, New York, NY"}]},{"given":"Shih-Fu","family":"Chang","sequence":"additional","affiliation":[{"name":"Columbia University, New York, NY"}]}],"member":"320","published-online":{"date-parts":[[2010,8,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1046920.1088704"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143857"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"Boyd S. and Vandenberghe L. 2003. Convex Optimization. Cambridge University Press.   Boyd S. and Vandenberghe L. 2003. Convex Optimization. Cambridge University Press.","DOI":"10.1017\/CBO9780511804441"},{"key":"e_1_2_1_4_1","unstructured":"Cox T. and Cox M. 1994. Multidimensional Scaling. Chapman &amp; Hall London.  Cox T. and Cox M. 1994. Multidimensional Scaling. Chapman &amp; Hall London."},{"key":"e_1_2_1_5_1","unstructured":"Dom B. E. 2001. An information-theoretic external cluster-validity measure. Res. rep. RJ 10219 IBM.  Dom B. E. 2001. An information-theoretic external cluster-validity measure. Res. rep. RJ 10219 IBM."},{"key":"e_1_2_1_6_1","volume-title":"Introduction to Statistical Pattern Recognition","author":"Fukunaga K.","unstructured":"Fukunaga , K. 1990. Introduction to Statistical Pattern Recognition . Elsevier . Fukunaga, K. 1990. Introduction to Statistical Pattern Recognition. 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