{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T06:25:37Z","timestamp":1649139937463},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"9-10","license":[{"start":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T00:00:00Z","timestamp":1576540800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T00:00:00Z","timestamp":1576540800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1007\/s11042-019-08285-7","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T01:02:23Z","timestamp":1576544543000},"page":"6689-6708","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic MLML-tree based adaptive object detection using heterogeneous data distribution"],"prefix":"10.1007","volume":"79","author":[{"given":"Dong Kyun","family":"Shin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minhaz Uddin","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeong Hyeon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phill Kyu","family":"Rhee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,17]]},"reference":[{"key":"8285_CR1","doi-asserted-by":"publisher","unstructured":"Dai J, Yan S, Tang X, Kwok JT (2006) Locally adaptive classification piloted by uncertainty. Proc. 23rd Int. Conf. Mach. Learn. - ICML \u201806 225\u2013232. doi: https:\/\/doi.org\/10.1145\/1143844.1143873","DOI":"10.1145\/1143844.1143873"},{"key":"8285_CR2","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/LGRS.2014.2351807","volume":"12","author":"K Ding","year":"2015","unstructured":"Ding K, Huo C, Xu Y, Zhong Z, Pan C (2015) Sparse hierarchical clustering for VHR image change detection. IEEE Geosci Remote Sens Lett 12:577\u2013581. https:\/\/doi.org\/10.1109\/LGRS.2014.2351807","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"8285_CR3","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1109\/TCSVT.2014.2355697","volume":"25","author":"J Dong","year":"2015","unstructured":"Dong J, Chen Q, Feng J, Jia K, Huang Z, Yan S (2015) Looking inside category: subcategory-aware object recognition. IEEE Trans Circuits Syst Video Technol 25:1322\u20131334. https:\/\/doi.org\/10.1109\/TCSVT.2014.2355697","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"8285_CR4","unstructured":"Du C, Zhu J, Zhang B (2015) Learning deep generative models with doubly stochastic MCMC"},{"key":"8285_CR5","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vis 88:303\u2013338. https:\/\/doi.org\/10.1007\/s11263-009-0275-4","journal-title":"Int J Comput Vis"},{"key":"8285_CR6","doi-asserted-by":"publisher","unstructured":"Everingham M, Winn J (2007) The PASCAL Visual Object Classes Challenge 2007 ( VOC2007 ) Development Kit. Challenge 2007, 1\u201323. https:\/\/doi.org\/10.1007\/s11263-009-0275-4","DOI":"10.1007\/s11263-009-0275-4"},{"key":"8285_CR7","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. IEEE Trans Image Process 26:1923\u20131938. https:\/\/doi.org\/10.1109\/TIP.2017.2667405","journal-title":"IEEE Trans Image Process"},{"key":"8285_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TPAMI.2009.167","volume":"32","author":"PF Felzenszwalb","year":"2009","unstructured":"Felzenszwalb PF, Girshick RB, Mcallester D, Ramanan D (2009) Object detection with discriminatively trained part based models. IEEE Trans Pattern Anal Mach Intell 32:1\u201320. https:\/\/doi.org\/10.1109\/TPAMI.2009.167","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"8285_CR9","doi-asserted-by":"publisher","first-page":"4163","DOI":"10.1109\/TSP.2012.2196696","volume":"60","author":"PA Forero","year":"2012","unstructured":"Forero PA, Kekatos V, Giannakis GB (2012) Robust clustering using outlier-sparsity regularization. IEEE Trans Signal Process 60:4163\u20134177. https:\/\/doi.org\/10.1109\/TSP.2012.2196696","journal-title":"IEEE Trans Signal Process"},{"key":"8285_CR10","doi-asserted-by":"publisher","unstructured":"Girshick R (2015) Fast R-CNN, in: Proceedings of the IEEE International Conference on Computer Vision. pp. 1440\u20131448. https:\/\/doi.org\/10.1109\/ICCV.2015.169","DOI":"10.1109\/ICCV.2015.169"},{"key":"8285_CR11","doi-asserted-by":"publisher","unstructured":"Hoiem D, Chodpathumwan Y, Dai Q (2012) Diagnosing error in object detectors. Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) 7574 LNCS, 340\u2013353. https:\/\/doi.org\/10.1007\/978-3-642-33712-3_25","DOI":"10.1007\/978-3-642-33712-3_25"},{"key":"8285_CR12","doi-asserted-by":"publisher","unstructured":"Hwang J, Kim J, Ahmadi A, Choi M, Tani J (2017) Predictive Coding-based Deep Dynamic Neural Network for Visuomotor Learning. arXiv.org. https:\/\/doi.org\/10.2307\/253568?ref=search-gateway:8d3451497faea721824565a745fa2fea","DOI":"10.2307\/253568?ref=search-gateway:8d3451497faea721824565a745fa2fea"},{"key":"8285_CR13","doi-asserted-by":"publisher","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet Classification with Deep Convolutional Neural Networks. Adv. Neural Inf. Process. Syst. 1\u20139. https:\/\/doi.org\/10.1016\/j.protcy.2014.09.007","DOI":"10.1016\/j.protcy.2014.09.007"},{"key":"8285_CR14","doi-asserted-by":"publisher","unstructured":"Lin TY, Goyal P, Girshick R, He K, Dollar P (2017) Focal Loss for Dense Object Detection. Proc. IEEE Int. Conf. Comput. Vis. 2017\u2013Octob, 2999\u20133007. https:\/\/doi.org\/10.1109\/ICCV.2017.324","DOI":"10.1109\/ICCV.2017.324"},{"key":"8285_CR15","doi-asserted-by":"publisher","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft COCO: Common objects in context, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 740\u2013755. https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"8285_CR16","doi-asserted-by":"publisher","unstructured":"Liu B, Sadeghi F, Tappen M, Shamir O, Liu C 2013 Probabilistic label trees for efficient large scale image classification. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit 843\u2013850. doi: https:\/\/doi.org\/10.1109\/CVPR.2013.114","DOI":"10.1109\/CVPR.2013.114"},{"key":"8285_CR17","doi-asserted-by":"publisher","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C, Berg AC (n.d.) SSD : Single Shot MultiBox Detector 1\u201315. https:\/\/doi.org\/10.1016\/j.nima.2015.05.028","DOI":"10.1016\/j.nima.2015.05.028"},{"key":"8285_CR18","doi-asserted-by":"publisher","unstructured":"Malisiewicz T, Gupta A, Efros AA (2011) Ensemble of exemplar-SVMs for object detection and beyond. Proc IEEE Int Conf Comput Vis 89\u201396. doi: https:\/\/doi.org\/10.1109\/ICCV.2011.6126229","DOI":"10.1109\/ICCV.2011.6126229"},{"key":"8285_CR19","doi-asserted-by":"publisher","unstructured":"Mordan T, Thome N, Henaff G, Cord M (2018) End-to-end learning of latent deformable part-based representations for object detection. International Journal of Computer Vision, 1\u201321. https:\/\/doi.org\/10.1007\/s11263-018-1109-z","DOI":"10.1007\/s11263-018-1109-z"},{"key":"8285_CR20","doi-asserted-by":"publisher","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2015) You only look Once: Unified, Real-Time Object Detection. https:\/\/doi.org\/10.1109\/CVPR.2016.91","DOI":"10.1109\/CVPR.2016.91"},{"key":"8285_CR21","doi-asserted-by":"publisher","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. https:\/\/doi.org\/10.1109\/CVPR.2017.690","DOI":"10.1109\/CVPR.2017.690"},{"key":"8285_CR22","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2016) YOLO9000: Better, Faster, Stronger","DOI":"10.1109\/CVPR.2017.690"},{"key":"8285_CR23","doi-asserted-by":"publisher","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. Nips:1\u201310. https:\/\/doi.org\/10.1016\/j.nima.2015.05.028","DOI":"10.1016\/j.nima.2015.05.028"},{"key":"8285_CR24","unstructured":"Roy D, Panda P, Roy K (2018) Tree-CNN: a hierarchical deep convolutional neural network for incremental learning 1\u201312."},{"key":"8285_CR25","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/LSP.2014.2349940","volume":"22","author":"Z Ruan","year":"2015","unstructured":"Ruan Z, Wang G, Xue JH, Lin X (2015) Subcategory clustering with latent feature alignment and filtering for object detection. IEEE Signal Process Lett 22:244\u2013248. https:\/\/doi.org\/10.1109\/LSP.2014.2349940","journal-title":"IEEE Signal Process Lett"},{"key":"8285_CR26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comput Vis 115:211\u2013252. https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int J Comput Vis"},{"key":"8285_CR27","doi-asserted-by":"publisher","first-page":"2579","DOI":"10.1007\/s10479-011-0841-3","volume":"9","author":"LJP Van Der Maaten","year":"2008","unstructured":"Van Der Maaten LJP, Hinton GE (2008) Visualizing high-dimensional data using t-sne. J Mach Learn Res 9:2579\u20132605. https:\/\/doi.org\/10.1007\/s10479-011-0841-3","journal-title":"J Mach Learn Res"},{"key":"8285_CR28","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1109\/TIP.2013.2295753","volume":"23","author":"L Wang","year":"2014","unstructured":"Wang L, Qiao Y, Tang X (2014) Latent hierarchical model of temporal structure for complex activity classification. Image Process IEEE Trans 23:810\u2013822. https:\/\/doi.org\/10.1109\/TIP.2013.2295753","journal-title":"Image Process IEEE Trans"},{"key":"8285_CR29","doi-asserted-by":"publisher","unstructured":"Wu S, Xu Y (2019) DSN: a new deformable subnetwork for object detection. IEEE Transactions on Circuits and Systems for Video Technology doi: https:\/\/doi.org\/10.1109\/TCSVT.2019.2905373","DOI":"10.1109\/TCSVT.2019.2905373"},{"key":"8285_CR30","doi-asserted-by":"publisher","first-page":"2665","DOI":"10.1109\/TKDE.2016.2581161","volume":"28","author":"Q Wu","year":"2016","unstructured":"Wu Q, Tan M, Song H, Chen J, Ng MK (2016) ML-FOREST: a multi-label tree ensemble method for multi-label classification. IEEE Trans Knowl Data Eng 28:2665\u20132680. https:\/\/doi.org\/10.1109\/TKDE.2016.2581161","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"8285_CR31","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1109\/WACV.2017.108","volume":"2017","author":"Y Xiang","year":"2017","unstructured":"Xiang Y, Choi W, Lin Y, Savarese S (2017) Subcategory-aware convolutional neural networks for object proposals & detection. Proc. - 2017 IEEE winter Conf. Appl. Comput. Vision, WACV 2017:924\u2013933. https:\/\/doi.org\/10.1109\/WACV.2017.108","journal-title":"Vision, WACV"},{"key":"8285_CR32","doi-asserted-by":"publisher","unstructured":"Yan Z, Zhang H, Piramuthu R, Jagadeesh V, DeCoste D, Di W, Yu Y (2014) HD-CNN: hierarchical deep convolutional neural network for large scale visual recognition 2740\u20132748. doi: https:\/\/doi.org\/10.1109\/ICCV.2015.314","DOI":"10.1109\/ICCV.2015.314"},{"key":"8285_CR33","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, Yi Y (2017) Deep learning hierarchical representations for image Steganalysis. IEEE Trans Inf Forensics Secur 12:2545\u20132557. https:\/\/doi.org\/10.1109\/TIFS.2017.2710946","journal-title":"IEEE Trans Inf Forensics Secur"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-08285-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11042-019-08285-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-019-08285-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,12,16]],"date-time":"2020-12-16T00:35:13Z","timestamp":1608078913000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11042-019-08285-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,17]]},"references-count":33,"journal-issue":{"issue":"9-10","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["8285"],"URL":"https:\/\/doi.org\/10.1007\/s11042-019-08285-7","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,17]]},"assertion":[{"value":"13 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 December 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}