{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:17:53Z","timestamp":1782836273958,"version":"3.54.5"},"reference-count":44,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T00:00:00Z","timestamp":1626912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61672265 and U1836218"],"award-info":[{"award-number":["61672265 and U1836218"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"111 Project of Chinese Ministry of Education","award":["B12018"],"award-info":[{"award-number":["B12018"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2021,8,31]]},"abstract":"<jats:p>\n            Hashing methods have sparked a great revolution on large-scale cross-media search due to its effectiveness and efficiency. Most existing approaches learn unified hash representation in a common Hamming space to represent all multimodal data. However, the unified hash codes may not characterize the cross-modal data discriminatively, because the data may vary greatly due to its different dimensionalities, physical properties, and statistical information. In addition, most existing supervised cross-modal algorithms preserve the similarity relationship by constructing an\n            <jats:italic>n<\/jats:italic>\n            \u00d7\n            <jats:italic>n<\/jats:italic>\n            pairwise similarity matrix, which requires a large amount of calculation and loses the category information. To mitigate these issues, a novel cross-media hashing approach is proposed in this article, dubbed label flexible matrix factorization hashing (LFMH). Specifically, LFMH jointly learns the modality-specific latent subspace with similar semantic by the flexible matrix factorization. In addition, LFMH guides the hash learning by utilizing the semantic labels directly instead of the large\n            <jats:italic>n<\/jats:italic>\n            \u00d7\n            <jats:italic>n<\/jats:italic>\n            pairwise similarity matrix. LFMH transforms the heterogeneous data into modality-specific latent semantic representation. Therefore, we can obtain the hash codes by quantifying the representations, and the learned hash codes are consistent with the supervised labels of multimodal data. Then, we can obtain the similar binary codes of the corresponding modality, and the binary codes can characterize such samples flexibly. Accordingly, the derived hash codes have more discriminative power for single-modal and cross-modal retrieval tasks. Extensive experiments on eight different databases demonstrate that our model outperforms some competitive approaches.\n          <\/jats:p>","DOI":"10.1145\/3446774","type":"journal-article","created":{"date-parts":[[2021,7,22]],"date-time":"2021-07-22T14:44:29Z","timestamp":1626965069000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":53,"title":["Label Consistent Flexible Matrix Factorization Hashing for Efficient Cross-modal Retrieval"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0716-0918","authenticated-orcid":false,"given":"Donglin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao-Jun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,22]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/553876"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939812"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1646396.1646452"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/997817.997857"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.267"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-013-0658-4"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2890144"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/1460096.1460104"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.348"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/2283516.2283623"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2016.2608906"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.672"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91458-9_37"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2013.06.011"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2355047"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/3287850.3287888"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.282"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.225"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/1866696.1866717"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1873951.1873987"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.5555\/3042817.3043085"},{"key":"e_1_2_1_23_1","volume-title":"Proceedings of the Conference on Data Mining and Data Warehouses (SiKDD\u201910)","author":"Rupnik Jan","year":"2010","unstructured":"Jan Rupnik and John Shawe-Taylor . 2010 . Multi-view canonical correlation analysis . In Proceedings of the Conference on Data Mining and Data Warehouses (SiKDD\u201910) . 1\u20134. Jan Rupnik and John Shawe-Taylor. 2010. Multi-view canonical correlation analysis. In Proceedings of the Conference on Data Mining and Data Warehouses (SiKDD\u201910). 1\u20134."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-007-0090-8"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2355114"},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the Constantinides International Workshop on Signal Processing.","author":"Shen Guan Lin","year":"2013","unstructured":"Guan Lin Shen and Xiao-Jun Wu . 2013 . Content-based image retrieval by combining color, texture, and CENTRIST . In Proceedings of the Constantinides International Workshop on Signal Processing. Guan Lin Shen and Xiao-Jun Wu. 2013. Content-based image retrieval by combining color, texture, and CENTRIST. 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