{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T11:13:22Z","timestamp":1780485202020,"version":"3.54.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T00:00:00Z","timestamp":1712793600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T00:00:00Z","timestamp":1712793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"General Program of Natural Science Foundation of Hunan Province","award":["2021JJ31164"],"award-info":[{"award-number":["2021JJ31164"]}]},{"name":"Key Program of Science Research Foundation of Education Department of Hunan Province","award":["22A0195"],"award-info":[{"award-number":["22A0195"]}]},{"name":"Teaching Reform Research Program of Education Department of Hunan Province","award":["HNJG-20230471"],"award-info":[{"award-number":["HNJG-20230471"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s13042-024-02154-y","type":"journal-article","created":{"date-parts":[[2024,4,11]],"date-time":"2024-04-11T08:02:04Z","timestamp":1712822524000},"page":"4423-4434","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Unsupervised deep hashing with multiple similarity preservation for cross-modal image-text retrieval"],"prefix":"10.1007","volume":"15","author":[{"given":"Siyu","family":"Xiong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueqiang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qinghua","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Beckman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,4,11]]},"reference":[{"issue":"9","key":"2154_CR1","doi-asserted-by":"publisher","first-page":"2372","DOI":"10.1109\/TCSVT.2017.2705068","volume":"28","author":"Y Peng","year":"2017","unstructured":"Peng Y, Huang X, Zhao Y (2017) An overview of cross-media retrieval: concepts, methodologies, benchmarks, and challenges. IEEE Trans Circuits Syst Video Technol 28(9):2372\u20132385. https:\/\/doi.org\/10.1109\/TCSVT.2017.2705068","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"2154_CR2","unstructured":"Wang K, Yin Q, Wang W et al (2016) A comprehensive survey on cross-modal retrieval. arXiv preprint arXiv:1607.06215"},{"key":"2154_CR3","doi-asserted-by":"publisher","unstructured":"Cao Y, Long M, Wang J et al (2016) Deep visual-semantic hashing for cross-modal retrieval. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1445\u20131454. https:\/\/doi.org\/10.1145\/2939672.2939812","DOI":"10.1145\/2939672.2939812"},{"key":"2154_CR4","unstructured":"Wang D, Gao X, Wang X et al (2015) Semantic topic multimodal hashing for cross-media retrieval. In: Twenty-fourth international joint conference on artificial intelligence, pp 3890\u20133896"},{"key":"2154_CR5","doi-asserted-by":"publisher","unstructured":"Huang S, Xiong Y, Zhang Y et al (2017) Unsupervised triplet hashing for fast image retrieval. In: proceedings of the on thematic workshops of ACM multimedia 2017, pp 84\u201392. https:\/\/doi.org\/10.1145\/3126686.3126773","DOI":"10.1145\/3126686.3126773"},{"key":"2154_CR6","doi-asserted-by":"publisher","unstructured":"Liu Z, Rodriguez-Opazo C, Teney D et al (2021) Image retrieval on real-life images with pre-trained vision-and-language models. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 2125\u20132134. https:\/\/doi.org\/10.1109\/iccv48922.2021.00213","DOI":"10.1109\/iccv48922.2021.00213"},{"issue":"2","key":"2154_CR7","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.32604\/cmc.2020.011706","volume":"65","author":"R Chen","year":"2020","unstructured":"Chen R, Pan L, Li C et al (2020) An improved deep fusion cnn for image recognition. Comput Mater Contin 65(2):1691\u20131706. https:\/\/doi.org\/10.32604\/cmc.2020.011706","journal-title":"Comput Mater Contin"},{"key":"2154_CR8","doi-asserted-by":"crossref","unstructured":"Cao M, Li S, Li J et al (2022) Image-text retrieval: a survey on recent research and development. arXiv preprint arXiv:2203.14713","DOI":"10.24963\/ijcai.2022\/759"},{"key":"2154_CR9","doi-asserted-by":"publisher","unstructured":"Zhang L, Chen L, Zhou C et al (2021) Exploring graph-structured semantics for cross-modal retrieval. In: Proceedings of the 29th ACM international conference on multimedia, pp 4277\u20134286. https:\/\/doi.org\/10.1145\/3474085.3475567","DOI":"10.1145\/3474085.3475567"},{"key":"2154_CR10","unstructured":"Shi Y, Chung Y (2022) Efficient cross-modal retrieval via deep binary hashing and quantization. arXiv preprint arXiv:2202.10232"},{"key":"2154_CR11","doi-asserted-by":"publisher","first-page":"106851","DOI":"10.1016\/j.knosys.2021.106851","volume":"219","author":"F Li","year":"2021","unstructured":"Li F, Wang T, Zhu L et al (2021) Task-adaptive asymmetric deep cross-modal hashing. Knowl-Based Syst 219:106851. https:\/\/doi.org\/10.1016\/j.knosys.2021.106851","journal-title":"Knowl-Based Syst"},{"key":"2154_CR12","doi-asserted-by":"publisher","unstructured":"Feng F, Wang X, Li R (2014) Cross-modal retrieval with correspondence autoencoder. In: Proceedings of the 22nd ACM international conference on multimedia, pp 7\u201316. https:\/\/doi.org\/10.1145\/2647868.2654902","DOI":"10.1145\/2647868.2654902"},{"key":"2154_CR13","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1016\/j.jvcir.2017.02.011","volume":"48","author":"B Jiang","year":"2017","unstructured":"Jiang B, Yang J, Lv Z et al (2017) Internet cross-media retrieval based on deep learning. J Vis Commun Image Represent 48:356\u2013366. https:\/\/doi.org\/10.1016\/j.jvcir.2017.02.011","journal-title":"J Vis Commun Image Represent"},{"issue":"4","key":"2154_CR14","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s13042-019-00962-1","volume":"11","author":"W Zheng","year":"2020","unstructured":"Zheng W, Liu H, Wang B et al (2020) Cross-modal learning for material perception using deep extreme learning machine. Int J Mach Learn Cybern 11(4):813\u2013823. https:\/\/doi.org\/10.1007\/s13042-019-00962-1","journal-title":"Int J Mach Learn Cybern"},{"key":"2154_CR15","doi-asserted-by":"publisher","first-page":"100336","DOI":"10.1016\/j.cosrev.2020.100336","volume":"39","author":"P Kaur","year":"2021","unstructured":"Kaur P, Pannu HS, Malhi AK (2021) Comparative analysis on cross-modal information retrieval: a review. Comput Sci Rev 39:100336. https:\/\/doi.org\/10.1016\/j.cosrev.2020.100336","journal-title":"Comput Sci Rev"},{"key":"2154_CR16","doi-asserted-by":"publisher","unstructured":"Wu G, Lin Z, Han J et al (2018) Unsupervised deep hashing via binary latent factor models for large-scale cross-modal retrieval. In: IJCAI, p 5. https:\/\/doi.org\/10.24963\/ijcai.2018\/396","DOI":"10.24963\/ijcai.2018\/396"},{"key":"2154_CR17","unstructured":"Radford A, Kim JW, Hallacy C et al (2021) Learning transferable visual models from natural language supervision. In: International conference on machine learning, pp 8748\u20138763"},{"key":"2154_CR18","doi-asserted-by":"publisher","unstructured":"Gu W, Gu X, Gu J et al (2019) Adversary guided asymmetric hashing for cross-modal retrieval. In: Proceedings of the 2019 on international conference on multimedia retrieval, pp 159\u2013167. https:\/\/doi.org\/10.1145\/3323873.3325045","DOI":"10.1145\/3323873.3325045"},{"key":"2154_CR19","doi-asserted-by":"publisher","unstructured":"Bai C, Zeng C, Ma Q et al (2020) Deep adversarial discrete hashing for cross-modal retrieval. In: Proceedings of the 2020 international conference on multimedia retrieval, pp 525\u2013531. https:\/\/doi.org\/10.1145\/3372278.3390711","DOI":"10.1145\/3372278.3390711"},{"key":"2154_CR20","doi-asserted-by":"publisher","first-page":"108343","DOI":"10.1016\/j.patcog.2021.108343","volume":"122","author":"D Zhang","year":"2022","unstructured":"Zhang D, Wu XJ (2022) Robust and discrete matrix factorization hashing for cross-modal retrieval. Pattern Recogn 122:108343. https:\/\/doi.org\/10.1016\/j.patcog.2021.108343","journal-title":"Pattern Recogn"},{"key":"2154_CR21","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.neucom.2022.01.078","volume":"496","author":"C Gu","year":"2022","unstructured":"Gu C, Bu J, Zhou X et al (2022) Cross-modal image retrieval with deep mutual information maximization. Neurocomputing 496:166\u2013177. https:\/\/doi.org\/10.1016\/j.neucom.2022.01.078","journal-title":"Neurocomputing"},{"key":"2154_CR22","doi-asserted-by":"publisher","unstructured":"Wang B, Yang Y, Xu X et al (2017) Adversarial cross-modal retrieval. In: Proceedings of the 25th ACM international conference on Multimedia, pp 154\u2013162. https:\/\/doi.org\/10.1145\/3123266.3123326","DOI":"10.1145\/3123266.3123326"},{"issue":"2","key":"2154_CR23","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1109\/TNNLS.2020.3029181","volume":"33","author":"L Zhen","year":"2020","unstructured":"Zhen L, Hu P, Peng X et al (2020) Deep multimodal transfer learning for cross-modal retrieval. IEEE Trans Neural Netw Learn Syst 33(2):798\u2013810. https:\/\/doi.org\/10.1109\/TNNLS.2020.3029181","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"6","key":"2154_CR24","doi-asserted-by":"publisher","first-page":"6461","DOI":"10.1109\/tkde.2022.3159131","volume":"35","author":"D Zhang","year":"2022","unstructured":"Zhang D, Wu XJ, Xu T et al (2022) Watch: two-stage discrete cross-media hashing. IEEE Trans Knowl Data Eng 35(6):6461\u20136474. https:\/\/doi.org\/10.1109\/tkde.2022.3159131","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2154_CR25","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.neucom.2020.03.019","volume":"400","author":"X Wang","year":"2020","unstructured":"Wang X, Zou X, Bakker EM et al (2020) Self-constraining and attention-based hashing network for bit-scalable cross-modal retrieval. Neurocomputing 400:255\u2013271. https:\/\/doi.org\/10.1016\/j.neucom.2020.03.019","journal-title":"Neurocomputing"},{"key":"2154_CR26","doi-asserted-by":"publisher","unstructured":"Su S, Zhong Z, Zhang C (2019) Deep joint-semantics reconstructing hashing for large-scale unsupervised cross-modal retrieval. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 3027\u20133035. https:\/\/doi.org\/10.1109\/iccv.2019.00312","DOI":"10.1109\/iccv.2019.00312"},{"key":"2154_CR27","doi-asserted-by":"publisher","unstructured":"Liu S, Qian S, Guan Y et al (2020) Joint-modal distribution-based similarity hashing for large-scale unsupervised deep cross-modal retrieval. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval, pp 1379\u20131388. https:\/\/doi.org\/10.1145\/3397271.3401086","DOI":"10.1145\/3397271.3401086"},{"key":"2154_CR28","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/s11280-020-00859-y","volume":"24","author":"PF Zhang","year":"2021","unstructured":"Zhang PF, Luo Y, Huang Z et al (2021) High-order nonlocal hashing for unsupervised cross-modal retrieval. World Wide Web 24:563\u2013583. https:\/\/doi.org\/10.1007\/s11280-020-00859-y","journal-title":"World Wide Web"},{"key":"2154_CR29","doi-asserted-by":"publisher","unstructured":"Yang D, Wu D, Zhang W et al (2020) Deep semantic-alignment hashing for unsupervised cross-modal retrieval. In: Proceedings of the 2020 international conference on multimedia retrieval, pp 44\u201352. https:\/\/doi.org\/10.1145\/3372278.3390673","DOI":"10.1145\/3372278.3390673"},{"key":"2154_CR30","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1109\/TMM.2021.3053766","volume":"24","author":"PF Zhang","year":"2021","unstructured":"Zhang PF, Li Y, Huang Z et al (2021) Aggregation-based graph convolutional hashing for unsupervised cross-modal retrieval. IEEE Trans Multimedia 24:466\u2013479. https:\/\/doi.org\/10.1109\/TMM.2021.3053766","journal-title":"IEEE Trans Multimedia"},{"key":"2154_CR31","doi-asserted-by":"publisher","unstructured":"Yu J, Zhou H, Zhan Y et al (2021) Deep graph-neighbor coherence preserving network for unsupervised cross-modal hashing. In: Proceedings of the AAAI conference on artificial intelligence, 35(5):4626\u20134634. https:\/\/doi.org\/10.1609\/aaai.v35i5.16592","DOI":"10.1609\/aaai.v35i5.16592"},{"key":"2154_CR32","doi-asserted-by":"crossref","unstructured":"Mikriukov G, Ravanbakhsh M, Demir B (2022) Deep unsupervised contrastive hashing for large-scale cross-modal text-image retrieval in remote sensing. arXiv preprint arXiv:2201.08125","DOI":"10.1109\/ICASSP43922.2022.9746251"},{"issue":"10","key":"2154_CR33","doi-asserted-by":"publisher","first-page":"7255","DOI":"10.1109\/TCSVT.2022.3172716","volume":"32","author":"Y Shi","year":"2022","unstructured":"Shi Y, Zhao Y, Liu X et al (2022) Deep adaptively-enhanced hashing with discriminative similarity guidance for unsupervised cross-modal retrieval. IEEE Trans Circuits Syst Video Technol 32(10):7255\u20137268. https:\/\/doi.org\/10.1109\/TCSVT.2022.3172716","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"6","key":"2154_CR34","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"2154_CR35","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"2154_CR36","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/LSP.2022.3148674","volume":"29","author":"X Luo","year":"2022","unstructured":"Luo X, Ma Z, Cheng W et al (2022) Improve deep unsupervised hashing via structural and intrinsic similarity learning. IEEE Signal Process Lett 29:602\u2013606. https:\/\/doi.org\/10.1109\/LSP.2022.3148674","journal-title":"IEEE Signal Process Lett"},{"key":"2154_CR37","unstructured":"Chen T, Kornblith S, Norouzi M et al (2020) A simple framework for contrastive learning of visual representations. In: International conference on machine learning, pp 1597\u20131607"},{"key":"2154_CR38","doi-asserted-by":"publisher","unstructured":"Rasiwasia N, Costa Pereira J, Coviello E et al (2010) A new approach to cross-modal multimedia retrieval. In: Proceedings of the 18th ACM international conference on multimedia, pp 251\u2013260. https:\/\/doi.org\/10.1145\/1873951.1873987","DOI":"10.1145\/1873951.1873987"},{"key":"2154_CR39","doi-asserted-by":"publisher","unstructured":"Huiskes MJ, Lew MS (2008) The mir flickr retrieval evaluation. In: Proceedings of the 1st ACM international conference on multimedia information retrieval, pp 39\u201343. https:\/\/doi.org\/10.1145\/1460096.1460104","DOI":"10.1145\/1460096.1460104"},{"key":"2154_CR40","doi-asserted-by":"publisher","unstructured":"Chua TS, Tang J, Hong R et al (2009) Nus-wide: a real-world web image database from national university of Singapore. In: Proceedings of the ACM international conference on image and video retrieval, pp 1\u20139. https:\/\/doi.org\/10.1145\/1646396.1646452","DOI":"10.1145\/1646396.1646452"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02154-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02154-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02154-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T16:31:28Z","timestamp":1726245088000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02154-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,11]]},"references-count":40,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["2154"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02154-y","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,11]]},"assertion":[{"value":"10 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 March 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 April 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competng fnancial interests or personal relatonships that could have appeared to infuence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}