{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:28:08Z","timestamp":1740122888932,"version":"3.37.3"},"reference-count":57,"publisher":"Springer Science and Business Media LLC","issue":"33-34","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Nature Science Foundation of China","doi-asserted-by":"crossref","award":["NO.61402368"],"award-info":[{"award-number":["NO.61402368"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Aerospace Science and Technology Innovation Foundation of China","award":["NO.2017ZD53047 and NO.20175896"],"award-info":[{"award-number":["NO.2017ZD53047 and NO.20175896"]}]},{"name":"Common Technology Foundation for Pre-research and Development of Equipment in the 13th Five-Year Plan","award":["NO.41412010402"],"award-info":[{"award-number":["NO.41412010402"]}]},{"name":"the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University","award":["NO.ZZ2019166"],"award-info":[{"award-number":["NO.ZZ2019166"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1007\/s11042-020-09090-3","type":"journal-article","created":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T06:32:31Z","timestamp":1593585151000},"page":"25189-25214","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A concept ontology triplet network for learning discriminative representations of fine-grained classes"],"prefix":"10.1007","volume":"79","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7267-2373","authenticated-orcid":false,"given":"Guiqing","family":"He","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haixi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuelei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,7,1]]},"reference":[{"issue":"4","key":"9090_CR1","doi-asserted-by":"publisher","first-page":"98:1","DOI":"10.1145\/2766959","volume":"34","author":"S Bell","year":"2015","unstructured":"Bell S, Bala K (2015) Learning visual similarity for product design with convolutional neural networks. Trans Graph 34(4):98:1\u201398:10","journal-title":"Trans Graph"},{"issue":"11","key":"9090_CR2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1142\/9789812797926_0003","volume":"86","author":"J Bromley","year":"1994","unstructured":"Bromley J, Bentz JW, Bottou L, Guyon I, Lecun Y, Moore C, S\u00e4ckinger E, Shah R (1994) Signature verification using a siamese time delay neural network. Series Mach Percep Artif Intell 86(11):25\u201344","journal-title":"Series Mach Percep Artif Intell"},{"key":"9090_CR3","doi-asserted-by":"crossref","unstructured":"Bucher M, Herbin S, Jurie F (2016) Improving semantic embedding consistency by metric learning for zero-shot classiffication. Computer Vision \u2013, ECCV, pp 730\u2013746","DOI":"10.1007\/978-3-319-46454-1_44"},{"key":"9090_CR4","doi-asserted-by":"crossref","unstructured":"Cheng D, Gong Y, Zhou S, Wang J, Zheng N (2016) Person re-identification by multi-channel parts-based cnn with improved triplet loss function. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.149"},{"key":"9090_CR5","doi-asserted-by":"crossref","unstructured":"Chen W, Chen X, Zhang J, Huang K (2017) Beyond triplet loss: a deep quadruplet network for person re-identification. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2017.145"},{"key":"9090_CR6","doi-asserted-by":"crossref","unstructured":"Chen W, Chen X, Zhang J, Huang K (2017) A multitask deep network for person re-identification AAAI","DOI":"10.1609\/aaai.v31i1.11201"},{"key":"9090_CR7","doi-asserted-by":"crossref","unstructured":"Chen Y, Jin X, Feng J, Yan S (2017) Training group orthogonal neural networks with privileged information. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence","DOI":"10.24963\/ijcai.2017\/212"},{"key":"9090_CR8","doi-asserted-by":"crossref","unstructured":"Dean T, Ruzon MA, Segal M, Shlens J, Vijayanarasimhan S, Yagnik J (2013) Fast, accurate detection of 100,000 object classes on a single machine. In: Conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2013.237"},{"key":"9090_CR9","doi-asserted-by":"crossref","unstructured":"Deng J, Ding N, Jia Y, Frome A, Murphy K, Bengio S, Li Y, Neven H, Ha A (2014) Large-scale object classification using label relation graphs. Computer Vision \u2013, ECCV, pp 48\u201364","DOI":"10.1007\/978-3-319-10590-1_4"},{"issue":"4","key":"9090_CR10","doi-asserted-by":"publisher","first-page":"1923","DOI":"10.1109\/TIP.2017.2667405","volume":"26","author":"J Fan","year":"2017","unstructured":"Fan J, Zhao T, Kuang Z, Zheng Y, Zhang J, Yu J, Peng J (2017) Hd-mtl: hierarchical deep multi-task learning for large-scale visual recognition. Trans Image Process 26(4):1923\u20131938","journal-title":"Trans Image Process"},{"key":"9090_CR11","doi-asserted-by":"crossref","unstructured":"Guo Y, Zhang L, Hu Y, He X, Gao J (2016) Msceleb-1m: a dataset and benchmark for large-scale face recognition. In: Computer vision \u2013, ECCV, pp 87\u2013102","DOI":"10.1007\/978-3-319-46487-9_6"},{"issue":"6","key":"9090_CR12","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.1109\/TMM.2014.2321530","volume":"16","author":"Y Han","year":"2014","unstructured":"Han Y, Wei X, Cao X et al (2014) Augmenting image descriptions using structured prediction output. IEEE Trans Multimed 16(6):1665\u20131676","journal-title":"IEEE Trans Multimed"},{"key":"9090_CR13","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.90"},{"key":"9090_CR14","unstructured":"Hermans A, Beyer L, Leibe B (2017) In defense of the triplet loss for person re-identification. Computer Vision and Pattern Recognition"},{"issue":"7","key":"9090_CR15","first-page":"38","volume":"14","author":"G Hinton","year":"2015","unstructured":"Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. Comput Sci 14(7):38\u201339","journal-title":"Comput Sci"},{"key":"9090_CR16","doi-asserted-by":"crossref","unstructured":"Hoffer E, Ailon N (2015) Deep metric learning using triplet network. Similarity-Based Pattern Recognition, 84\u201392","DOI":"10.1007\/978-3-319-24261-3_7"},{"key":"9090_CR17","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2017.243"},{"key":"9090_CR18","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 60:84\u201390","journal-title":"Commun ACM"},{"key":"9090_CR19","doi-asserted-by":"crossref","unstructured":"Kuang Z, Li Z, Zhao T, Fan J (2017) Deep multi-task learning for large-scale image classification. In: Third international conference on multimedia big data (BigMM)","DOI":"10.1109\/BigMM.2017.72"},{"key":"9090_CR20","doi-asserted-by":"crossref","unstructured":"Learned-Miller E, Huang GB, RoyChowdhury A, Li H, Hua G (2016) Labeled faces in the wild: a survey. Advances in Face Detection and Facial Image Analysis, 189\u2013248","DOI":"10.1007\/978-3-319-25958-1_8"},{"issue":"11","key":"9090_CR21","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"9090_CR22","unstructured":"Lin M, Chen Q, Yan S (2013) Network in network CoRR"},{"key":"9090_CR23","doi-asserted-by":"crossref","unstructured":"Lin T-Y, RoyChowdhury A, Maji S (2015) Bilinear cnn models for fine-grained visual recognition. International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV.2015.170"},{"key":"9090_CR24","doi-asserted-by":"crossref","unstructured":"Parkhi OM, Vedaldi A, Zisserman A (2015) Deep face recognition. In: Procedings of the British machine vision conference","DOI":"10.5244\/C.29.41"},{"key":"9090_CR25","doi-asserted-by":"crossref","unstructured":"Razavian AS, Azizpour H, Sullivan J, Carlsson S (2014) Cnn features off-the-shelf: an astounding baseline for recognition. In: Conference on computer vision and pattern recognition workshops","DOI":"10.1109\/CVPRW.2014.131"},{"key":"9090_CR26","unstructured":"Roy D, Panda P, Roy K (2018) Tree-cnn: a hierarchical deep convolutional neural network for incremental learning. Proc IEEE"},{"key":"9090_CR27","doi-asserted-by":"crossref","unstructured":"Sankaranarayanan S, Alavi A, Castillo CD, Chellappa R (2016) Triplet probabilistic embedding for face verification and clustering. In: 8th International conference on biometrics theory applications and systems (BTAS)","DOI":"10.1109\/BTAS.2016.7791205"},{"key":"9090_CR28","doi-asserted-by":"crossref","unstructured":"Schroff F, Kalenichenko D, Philbin J (2015) Facenet: a unified embedding for face recognition and clustering. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"9090_CR29","unstructured":"Sebastian R (2017) An overview of multi-task learning in deep neural networks arXiv: learning"},{"key":"9090_CR30","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. Int Conf Learn Represent"},{"key":"9090_CR31","doi-asserted-by":"crossref","unstructured":"Song HO, Xiang Y, Jegelka S, Savarese S (2016) Deep metric learning via lifted structured feature embedding. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.434"},{"key":"9090_CR32","unstructured":"Sohn K (2016) Improved deep metric learning with multi-class n-pair loss objectiv. NIPS, pp 1857\u20131865"},{"key":"9090_CR33","unstructured":"Sukhbaatar S, Bruna J, Paluri M, Bourdev LD, Fergus R (2014) Training convolutional networks with noisy labels. Computer Vision and Pattern Recognition"},{"key":"9090_CR34","doi-asserted-by":"crossref","unstructured":"Sun M, Huang W, Savarese S (2013) Find the best path: an efficient and accurate classifier for image hierarchies. International Conference on Computer Vision","DOI":"10.1109\/ICCV.2013.40"},{"key":"9090_CR35","doi-asserted-by":"crossref","unstructured":"Szegedy C et al (2015) Going deeper with convolutions. In: Conference on computer vision and pattern recognition(CVPR)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"9090_CR36","doi-asserted-by":"crossref","unstructured":"Wang J, Song Y, Leung T, Rosenberg C, Wang J, Philbin J, Chen B, Wu Y (2014) Learning fine-grained image similarity with deep ranking. In: Conference on compute vision and pattern recognition","DOI":"10.1109\/CVPR.2014.180"},{"key":"9090_CR37","doi-asserted-by":"crossref","unstructured":"Wang C, Lan X, Zhang X (2017) How to train triplet networks with 100k identities. In: Conference on computer vision workshops (ICCVW)","DOI":"10.1109\/ICCVW.2017.225"},{"key":"9090_CR38","doi-asserted-by":"crossref","unstructured":"Wang Q, Wan J, Li X (2018) Robust hierarchical deep learning for vehicular management. IEEE Transactions on Vehicular Technology (T-IV)","DOI":"10.1109\/TVT.2018.2883046"},{"key":"9090_CR39","unstructured":"Wang Q, Chen M, Nie F, Li X (2018) Detecting coherent groups in crowd scenes by multiview clustering. In: IEEE Transactions on pattern analysis and machine intelligence (T-PAMI)"},{"issue":"1","key":"9090_CR40","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/TGRS.2018.2849692","volume":"57","author":"Q Wang","year":"2019","unstructured":"Wang Q, Yuan Z, Li X (2019) Getnet: a general end-to-end two-dimensional cnn framework for hyperspectral image change detection. IEEE Trans Geosci Remote Sens (T-GRS) 57(1):3\u201313","journal-title":"IEEE Trans Geosci Remote Sens (T-GRS)"},{"issue":"2","key":"9090_CR41","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","volume":"57","author":"Q Wang","year":"2019","unstructured":"Wang Q, Liu S, Chanussot J, Li X (2019) Scene classification with recurrent attention of vhr remote sensing images. IEEE Trans Geosci Remote Sens (T-GRS) 57(2):1155\u20131167","journal-title":"IEEE Trans Geosci Remote Sens (T-GRS)"},{"key":"9090_CR42","doi-asserted-by":"crossref","unstructured":"Wu C-Y, Manmatha R, Smola AJ, Krahenbuhl P (2017) Sampling matters in deep embedding learning. In: International conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2017.309"},{"key":"9090_CR43","unstructured":"Xia Z, Hong X, Gao X, Feng X, Zhao G (2019) Spatiotemporal recurrent convolutional networks for recognizing spontaneous micro-expressions. IEEE Trans Multimed, 1\u20131"},{"key":"9090_CR44","doi-asserted-by":"crossref","unstructured":"Yan Z, Zhang H, Piramuthu R, Jagadeesh V, DeCoste D, Di W, Yu Y (2015) Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition. In: International conference on computer vision (ICCV)","DOI":"10.1109\/ICCV.2015.314"},{"issue":"11","key":"9090_CR45","doi-asserted-by":"publisher","first-page":"2545","DOI":"10.1109\/TIFS.2017.2710946","volume":"12","author":"J Ye","year":"2017","unstructured":"Ye J, Ni J et al (2017) Deep learning hierarchical representations for image steganalysis. IEEE Trans Inform Forens Secur 12(11):2545\u20132557","journal-title":"IEEE Trans Inform Forens Secur"},{"issue":"1","key":"9090_CR46","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s00521-014-1567-3","volume":"27","author":"L Ying","year":"2014","unstructured":"Ying L (2014) Orthogonal incremental extreme learning machine for regression and multiclass classification. Neural Comput and Applic 27(1):111\u2013120","journal-title":"Neural Comput and Applic"},{"issue":"5","key":"9090_CR47","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1109\/TIP.2014.2311377","volume":"23","author":"J Yu","year":"2014","unstructured":"Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process (TIP) 23 (5):2019\u20132032","journal-title":"IEEE Trans Image Process (TIP)"},{"issue":"1","key":"9090_CR48","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1109\/TMM.2013.2284755","volume":"16","author":"J Yu","year":"2014","unstructured":"Yu J, Rui Y, Chen B (2014) Exploiting click constraints and multiview features for image reranking. IEEE Trans Multimed 16(1):159\u2013168","journal-title":"IEEE Trans Multimed"},{"issue":"4","key":"9090_CR49","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1109\/TCYB.2014.2336697","volume":"45","author":"J Yu","year":"2015","unstructured":"Yu J, Tao D, Rui Y, Wang M (2015) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern (IEEE TCYB) 45 (4):767\u2013779","journal-title":"IEEE Trans Cybern (IEEE TCYB)"},{"key":"9090_CR50","doi-asserted-by":"crossref","unstructured":"Yu J, Kuang Z, Zhang B, Zhang W, Lin D, Fan J (2018) Leveraging content sensitiveness and user trustworthiness to recommend fine-grained privacy settings for social image sharing. IEEE Transactions on Information Forensics and Security","DOI":"10.1109\/TIFS.2017.2787986"},{"key":"9090_CR51","doi-asserted-by":"crossref","unstructured":"Zhang S, Gong Y, Wang J (2016) Deep metric learning with improved triplet loss for face clustering in videos. Lect Notes Comput Sci, 497\u2013508","DOI":"10.1007\/978-3-319-48890-5_49"},{"key":"9090_CR52","doi-asserted-by":"crossref","unstructured":"Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. Computer Vision\u2013 ECCV, 818\u2013833","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"9090_CR53","unstructured":"Zhao F, Huang Y, Wang L, Tan T (2015) Deep semantic ranking based hashing for multi-label image retrieval. In: Conference on computer vision and pattern recognition (CVPR)"},{"key":"9090_CR54","doi-asserted-by":"crossref","unstructured":"Zhang X, Zhou F, Lin Y et al (2016) Embedding label structures for fine-grained feature representation. CVPR, 1114\u20131123","DOI":"10.1109\/CVPR.2016.126"},{"key":"9090_CR55","doi-asserted-by":"crossref","unstructured":"Zhang H, He G, Peng J, Kuang Z, Fan J (2018) Deep learning of path-based tree classifiers for large-scale plant species identification. In: Conference on multimedia information processing and retrieval (MIPR)","DOI":"10.1109\/MIPR.2018.00013"},{"issue":"2","key":"9090_CR56","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TCYB.2015.2403356","volume":"46","author":"X Zhu","year":"2016","unstructured":"Zhu X, Li X, Zhang S (2016) Block-row sparse multiview multilabel learning for image classification. Trans Cybern 46(2):450\u2013461","journal-title":"Trans Cybern"},{"key":"9090_CR57","doi-asserted-by":"crossref","unstructured":"Zhuang B, Lin G, Shen C, Reid I (2016) Fast training of triplet-based deep binary embedding networks. In: Conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2016.641"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09090-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09090-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09090-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T23:42:47Z","timestamp":1723160567000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09090-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,1]]},"references-count":57,"journal-issue":{"issue":"33-34","published-print":{"date-parts":[[2020,9]]}},"alternative-id":["9090"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09090-3","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2020,7,1]]},"assertion":[{"value":"20 May 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with Ethical Standards"}},{"value":"No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interests"}}]}}