{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:13:13Z","timestamp":1750306393166,"version":"3.41.0"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2016,7,27]],"date-time":"2016-07-27T00:00:00Z","timestamp":1469577600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"State Key Development Program for Basic Research of China","award":["2014CB340304"],"award-info":[{"award-number":["2014CB340304"]}]},{"name":"Hong Kong RGC","award":["621013, 620812, and 621211"],"award-info":[{"award-number":["621013, 620812, and 621211"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2016,7,27]]},"abstract":"<jats:p>\n            Multi-modal similarity search has attracted considerable attention to meet the need of information retrieval across different types of media. To enable efficient multi-modal similarity search in large-scale databases recently, researchers start to study multi-modal hashing. Most of the existing methods are applied to search across multi-views among which explicit correspondence is provided. Given a multi-modal similarity search task, we observe that abundant multi-view data can be found on the Web which can serve as an auxiliary bridge. In this paper, we propose a\n            <jats:italic>Heterogeneous Translated Hashing<\/jats:italic>\n            (HTH) method with such auxiliary bridge incorporated not only to improve current multi-view search but also to enable similarity search across heterogeneous media which have no direct correspondence. HTH provides more flexible and discriminative ability by embedding heterogeneous media into different Hamming spaces, compared to almost all existing methods that map heterogeneous data in a common Hamming space. We formulate a joint optimization model to learn hash functions embedding heterogeneous media into different Hamming spaces, and a translator aligning different Hamming spaces. The extensive experiments on two real-world datasets, one publicly available dataset of Flickr, and the other MIRFLICKR-Yahoo Answers dataset, highlight the effectiveness and efficiency of our algorithm.\n          <\/jats:p>","DOI":"10.1145\/2744204","type":"journal-article","created":{"date-parts":[[2016,7,27]],"date-time":"2016-07-27T15:32:16Z","timestamp":1469633536000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Heterogeneous Translated Hashing"],"prefix":"10.1145","volume":"10","author":[{"given":"Ying","family":"Wei","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong"}]},{"given":"Yangqiu","family":"Song","sequence":"additional","affiliation":[{"name":"University of Illinois at Urbana-Champaign, Urbana, IL"}]},{"given":"Yi","family":"Zhen","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, GA"}]},{"given":"Bo","family":"Liu","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong"}]},{"given":"Qiang","family":"Yang","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2016,7,27]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2006.49"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/361002.361007"},{"key":"e_1_2_1_3_1","first-page":"351","article-title":"Convergence of alternating optimization","volume":"11","author":"Bezdek James C.","year":"2003","unstructured":"James C. 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